From 361b63d1448cb9a704ed837695eb384a9f4aad99 Mon Sep 17 00:00:00 2001
From: punkduckable <punkduckable@users.noreply.github.com>
Date: Mon, 28 Oct 2024 20:54:29 +0000
Subject: [PATCH] deploy: 9dcef3fb224b37abee10533af57696c3d2e262b8

---
 .doctrees/autoapi/lasdi/gp/index.doctree      | Bin 13189 -> 37256 bytes
 .doctrees/autoapi/lasdi/inputs/index.doctree  | Bin 18212 -> 29566 bytes
 .../autoapi/lasdi/latent_space/index.doctree  | Bin 35604 -> 65475 bytes
 .doctrees/autoapi/lasdi/param/index.doctree   | Bin 37799 -> 98214 bytes
 .doctrees/environment.pickle                  | Bin 930681 -> 1187015 bytes
 .doctrees/index.doctree                       | Bin 9071 -> 8119 bytes
 _sources/autoapi/lasdi/gp/index.rst.txt       |  69 +++++-
 _sources/autoapi/lasdi/inputs/index.rst.txt   |  61 +++--
 .../autoapi/lasdi/latent_space/index.rst.txt  |  99 +++++++-
 _sources/autoapi/lasdi/param/index.rst.txt    | 222 +++++++++++++++--
 _sources/index.rst.txt                        |   3 +
 autoapi/lasdi/gp/index.html                   |  77 ++++--
 autoapi/lasdi/inputs/index.html               |  70 +++---
 autoapi/lasdi/latent_space/index.html         |  97 +++++---
 autoapi/lasdi/param/index.html                | 225 ++++++++++++++----
 genindex.html                                 |  20 +-
 index.html                                    |  19 +-
 objects.inv                                   | Bin 2277 -> 2235 bytes
 searchindex.js                                |   2 +-
 19 files changed, 746 insertions(+), 218 deletions(-)

diff --git a/.doctrees/autoapi/lasdi/gp/index.doctree b/.doctrees/autoapi/lasdi/gp/index.doctree
index f0b3452bbff711a03a0247b858471c455da83d63..abe324ffc0a2a3bb5668c01c5b014f9dc8e19aae 100644
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diff --git a/.doctrees/autoapi/lasdi/latent_space/index.doctree b/.doctrees/autoapi/lasdi/latent_space/index.doctree
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diff --git a/_sources/autoapi/lasdi/gp/index.rst.txt b/_sources/autoapi/lasdi/gp/index.rst.txt
index ebedee2..7cedd0b 100644
--- a/_sources/autoapi/lasdi/gp/index.rst.txt
+++ b/_sources/autoapi/lasdi/gp/index.rst.txt
@@ -17,27 +17,76 @@ Functions
 Module Contents
 ---------------
 
-.. py:function:: fit_gps(X, Y)
+.. py:function:: fit_gps(X: numpy.ndarray, Y: numpy.ndarray) -> list[sklearn.gaussian_process.GaussianProcessRegressor]
+
+   Trains a GP for each column of Y. If Y has shape N x k, then we train k GP regressors. In this
+   case, we assume that X has shape N x M. Thus, the Input to the GP is in \mathbb{R}^M. For each
+   k, we train a GP where the i'th row of X is the input and the i,k component of Y is the
+   corresponding target. Thus, we return a list of k GP Regressor objects, the k'th one of which
+   makes predictions for the k'th coefficient in the latent dynamics.
 
-   Trains each GP given the interpolation dataset.
-   X: (n_train, n_param) numpy 2d array
-   Y: (n_train, n_coef) numpy 2d array
    We assume each target coefficient is independent with each other.
-   gp_dictionnary is a dataset containing the trained GPs (as sklearn objects)
 
 
 
-.. py:function:: eval_gp(gp_dictionnary, param_grid)
+   -----------------------------------------------------------------------------------------------
+   :Parameters: * **X** (*A 2d numpy array of shape (n_train, input_dim), where n_train is the number of training*)
+                * **examples and input_dim is the number of components in each input (e.g., the number of**
+                * **parameters)**
+                * **Y** (*A 2d numpy array of shape (n_train, n_coef), where n_train is the number of training*)
+                * **examples and n_coef is the number of coefficients in the latent dynamics.**
+
+   -----------------------------------------------------------------------------------------------
+   :returns: * *A list of trained GP regressor objects. If Y has k columns, then the returned list has k*
+             * *elements. It's i'th element holds a trained GP regressor object whose training inputs are the*
+             * *columns of X and whose corresponding target values are the elements of the i'th column of Y.*
+
+
+.. py:function:: eval_gp(gp_list: list[sklearn.gaussian_process.GaussianProcessRegressor], param_grid: numpy.ndarray) -> tuple
 
    Computes the GPs predictive mean and standard deviation for points of the parameter space grid
 
 
 
-.. py:function:: sample_coefs(gp_dictionnary, param, n_samples)
+   -----------------------------------------------------------------------------------------------
+   :Parameters: * **gp_list** (*a list of trained GP regressor objects. The number of elements in this list should*)
+                * **match the number of columns in param_grid. The i'th element of this list is a GP regressor**
+                * **object that predicts the i'th coefficient.**
+                * **param_grid** (*A 2d numpy.ndarray object of shape (number of parameter combination, number of*)
+                * **parameters). The i,j element of this array specifies the value of the j'th parameter in the**
+                * **i'th combination of parameters. We use this as the testing set for the GP evaluation.**
+
+   -----------------------------------------------------------------------------------------------
+   :returns: * *A two element tuple. Both are 2d numpy arrays of shape (number of parameter combinations,*
+             * *number of coefficients). The two arrays hold the predicted means and std's for each parameter*
+             * *at each training example, respectively.*
+             * *Thus, the i,j element of the first return variable holds the predicted mean of the j'th*
+             * *coefficient in the latent dynamics at the i'th training example. Likewise, the i,j element of*
+             * *the second return variable holds the standard deviation in the predicted distribution for the*
+             * *j'th coefficient in the latent dynamics at the i'th combination of parameter values.*
+
+
+.. py:function:: sample_coefs(gp_list: list[sklearn.gaussian_process.GaussianProcessRegressor], param: numpy.ndarray, n_samples: int)
+
+   Generates sets of ODE (SINDy) coefficients sampled from the predictive distribution for those
+   coefficients at the specified parameter value (parma). Specifically, for the k'th SINDy
+   coefficient, we draw n_samples samples of the predictive distribution for the k'th coefficient
+   when param is the parameter.
+
+
 
-   Generates sample sets of ODEs for one given parameter.
-   coef_samples is a list of length n_samples, where each terms is a matrix of SINDy coefficients sampled from the GP predictive
-   distributions
+   -----------------------------------------------------------------------------------------------
+   :Parameters: * **gp_list** (*a list of trained GP regressor objects. The number of elements in this list should*)
+                * **match the number of columns in param_grid. The i'th element of this list is a GP regressor**
+                * **object that predicts the i'th coefficient.**
+                * **param** (*A combination of parameter values. i.e., a single test example. We evaluate each GP in*)
+                * **the gp_list at this parameter value (getting a prediction for each coefficient).**
+                * **n_samples** (*Number of samples of the predicted latent dynamics used to build ensemble of fom*)
+                * **predictions. N_s in the paper.**
 
+   -----------------------------------------------------------------------------------------------
+   :returns: * *A 2d numpy ndarray object called coef_samples. It has shape (n_samples, n_coef), where n_coef*
+             * *is the number of coefficients (length of gp_list). The i,j element of this list is the i'th*
+             * *sample of the j'th SINDy coefficient.*
 
 
diff --git a/_sources/autoapi/lasdi/inputs/index.rst.txt b/_sources/autoapi/lasdi/inputs/index.rst.txt
index e9a51ca..9d6839f 100644
--- a/_sources/autoapi/lasdi/inputs/index.rst.txt
+++ b/_sources/autoapi/lasdi/inputs/index.rst.txt
@@ -20,46 +20,71 @@ Classes
    lasdi.inputs.InputParser
 
 
-Functions
----------
-
-.. autoapisummary::
-
-   lasdi.inputs.getDictFromList
-
-
 Module Contents
 ---------------
 
 .. py:data:: verbose
+   :type:  bool
    :value: False
 
 
-.. py:class:: InputParser(dict, name='')
+.. py:class:: InputParser(dict: InputParser.__init__.dict, name: str = '')
+
+   A InputParser objects acts as a wrapper around a dictionary of settings. Thus, each setting is
+   a key and the corresponding value is the setting's value. Because one setting may itself be
+   a dictionary (we often group settings; each group has a name but several constituent settings),
+   the underlying dictionary is structured as a sequence of nested dictionaries. This class allows
+   the user to select a specific setting from that structure by specifying (via a list of strings)
+   where in that nested structure the desired setting lives.
+
 
    .. py:attribute:: dict_
+      :type:  dict
       :value: None
 
 
 
    .. py:attribute:: name
+      :type:  str
       :value: ''
 
 
 
-   .. py:method:: getInput(keys, fallback=None, datatype=None)
+   .. py:method:: getInput(keys: list, fallback=None, datatype=None)
+
+      A InputParser object acts as a wrapper around a dictionary of settings. That is, self.dict_
+      is structured as a nested family of dictionaries. Each setting corresponds to a key in
+      self.dict_. The setting's value is the corresponding value in self.dict_. In many cases,
+      a particular setting may be nested within others. That is, a setting's value may itself be
+      another dictionary housing various sub-settings. This function allows us to fetch a
+      specific setting from this nested structure.
+
+      Specifically, we specify a list of strings. keys[0] should be a key in self.dict_
+      If so, we set val = self.dict_[keys[0]]. If there are more keys, then val should be a
+      dictionary and keys[1] should be a key in this dictionary. In this case, we replace val
+      with val[key[1]] and so on. This continues until we have exhausted all keys. There is one
+      important exception:
+
+          If at some point in the process, there are more keys but val is not a dictionary, or if
+          there are more keys and val is a dictionary but the next key is not a key in that
+          dictionary, then we return the fallback value. If the fallback value does not exist,
+          returns an error.
 
-      Find the value corresponding to the list of keys.
-      If the specified keys do not exist, use the fallback value.
-      If the fallback value does not exist, returns an error.
-      If the datatype is specified, enforce the output value has the right datatype.
 
 
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **keys** (*A list of keys we want to fetch from self.dict. keys[0] should be a key in self.dict_*)
+                   * **If so, we set val = self.dict_[keys[0]]. If there are more keys, then val should be a**
+                   * **dictionary and keys[1] should be a key in this dictionary. In this case, we replace val**
+                   * **with val[key[1]] and so on. This continues until we have exhausted all keys.**
+                   * **fallback** (*A sort of default value. If at some point, val is not a dictionary (and there are*)
+                   * **more keys) or val is a dictionary but the next key is not a valid key in that dictionary,**
+                   * **then we return the fallback value.**
+                   * **datatype** (*If not None, then we require that the final val has this datatype. If the final*)
+                   * **val does not have the desired datatype, we raise an exception.**
 
-.. py:function:: getDictFromList(list_, inputDict)
+      -------------------------------------------------------------------------------------------
+      :rtype: The final val value as outlined by the process described above.
 
-   get a dict with {key: val} from a list of dicts
-   NOTE: it returns only the first item in the list,
-   even if the list has more than one dict with {key: val}.
 
 
diff --git a/_sources/autoapi/lasdi/latent_space/index.rst.txt b/_sources/autoapi/lasdi/latent_space/index.rst.txt
index 4e1ef6e..b0033e1 100644
--- a/_sources/autoapi/lasdi/latent_space/index.rst.txt
+++ b/_sources/autoapi/lasdi/latent_space/index.rst.txt
@@ -34,63 +34,103 @@ Module Contents
 
 .. py:data:: act_dict
 
-.. py:function:: initial_condition_latent(param_grid, physics, autoencoder)
+.. py:function:: initial_condition_latent(param_grid: numpy.ndarray, physics: lasdi.physics.Physics, autoencoder: torch.nn.Module) -> list[numpy.ndarray]
 
-   Outputs the initial condition in the latent space: Z0 = encoder(U0)
+   This function maps a set of initial conditions for the fom to initial conditions for the
+   latent space dynamics. Specifically, we take in a set of possible parameter values. For each
+   set of parameter values, we recover the fom IC (from physics), then map this fom IC to a
+   latent space IC (by encoding it using the autoencoder). We do this for each parameter
+   combination and then return a list housing the latent space ICs.
 
 
+   -----------------------------------------------------------------------------------------------
+   :Parameters: * **param_grid** (*A 2d numpy.ndarray object of shape (number of parameter combination) x (number of*)
+                * **parameters).**
+                * **physics** (*A "Physics" object that stores the datasets for each parameter combination.*)
+                * **autoencoder** (*The actual autoencoder object that we use to map the ICs into the latent space.*)
 
-.. py:class:: MultiLayerPerceptron(layer_sizes, act_type='sigmoid', reshape_index=None, reshape_shape=None, threshold=0.1, value=0.0, num_heads=1)
+   -----------------------------------------------------------------------------------------------
+   :returns: * *A list of numpy ndarray objects whose i'th element holds the latent space initial condition*
+             * *for the i'th set of parameters in the param_grid. That is, if we let U0_i denote the fom IC for*
+             * *the i'th set of parameters, then the i'th element of the returned list is Z0_i = encoder(U0_i).*
+
+
+.. py:class:: MultiLayerPerceptron(layer_sizes: list[int], act_type: str = 'sigmoid', reshape_index: int = None, reshape_shape: tuple[int] = None, threshold: float = 0.1, value: float = 0.0)
 
    Bases: :py:obj:`torch.nn.Module`
 
 
    .. py:attribute:: n_layers
+      :type:  int
 
 
    .. py:attribute:: layer_sizes
+      :type:  list[int]
 
 
-   .. py:attribute:: fcs
+   .. py:attribute:: layers
+      :type:  list[torch.nn.Module]
       :value: []
 
 
 
    .. py:attribute:: reshape_index
+      :type:  int
 
 
    .. py:attribute:: reshape_shape
+      :type:  list[int]
 
 
    .. py:attribute:: act_type
+      :type:  str
+
+
+   .. py:method:: forward(x: torch.Tensor) -> torch.Tensor
+
+      This function defines the forward pass through self.
+
 
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **x** (*A tensor holding a batch of inputs. We pass this tensor through the network's layers*)
+                   * **and then return the result. If self.reshape_index == 0 and self.reshape_shape has k**
+                   * **elements, then the final k elements of x's shape must match self.reshape_shape.**
 
-   .. py:attribute:: use_multihead
-      :value: False
+      -------------------------------------------------------------------------------------------
+      :returns: * *The image of x under the network's layers. If self.reshape_index == -1 and*
+                * *self.reshape_shape has k elements, then we reshape the output so that the final k elements*
+                * *of its shape match those of self.reshape_shape.*
 
 
 
-   .. py:method:: forward(x)
+   .. py:method:: init_weight() -> None
 
+      This function initializes the weight matrices and bias vectors in self's layers.
 
-   .. py:method:: apply_attention(x, act_idx)
 
+      -------------------------------------------------------------------------------------------
+      :Parameters: **None!**
 
-   .. py:method:: init_weight()
+      -------------------------------------------------------------------------------------------
+      :rtype: Nothing!
 
 
-.. py:class:: Autoencoder(physics, config)
+
+.. py:class:: Autoencoder(physics: lasdi.physics.Physics, config: dict)
 
    Bases: :py:obj:`torch.nn.Module`
 
 
    .. py:attribute:: qgrid_size
+      :type:  list[int]
 
 
    .. py:attribute:: space_dim
+      :type:  numpy.ndarray
 
 
    .. py:attribute:: n_z
+      :type:  int
 
 
    .. py:attribute:: encoder
@@ -99,12 +139,45 @@ Module Contents
    .. py:attribute:: decoder
 
 
-   .. py:method:: forward(x)
+   .. py:method:: forward(x: torch.Tensor) -> torch.Tensor
+
+      This function defines the forward pass through self.
+
+
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **x** (*A tensor holding a batch of inputs. We pass this tensor through the encoder + decoder*)
+                   * **and then return the result.**
+
+      -------------------------------------------------------------------------------------------
+      :rtype: The image of x under the encoder + decoder.
+
+
+
+   .. py:method:: export() -> dict
+
+      This function extracts self's parameters and returns them in a dictionary.
+
+
+      -------------------------------------------------------------------------------------------
+      :Parameters: **None!**
+
+      -------------------------------------------------------------------------------------------
+      :rtype: The A dictionary housing self's state dictionary.
+
+
+
+   .. py:method:: load(dict_: dict) -> None
+
+      This function loads self's state dictionary.
 
 
-   .. py:method:: export()
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **dict_** (*This should be a dictionary with the key "autoencoder_param" whose corresponding*)
+                   * **value is the state dictionary of an autoencoder which has the same architecture (i.e.,**
+                   * **layer sizes) as self.**
 
+      -------------------------------------------------------------------------------------------
+      :rtype: Nothing!
 
-   .. py:method:: load(dict_)
 
 
diff --git a/_sources/autoapi/lasdi/param/index.rst.txt b/_sources/autoapi/lasdi/param/index.rst.txt
index dfc9691..5dfda82 100644
--- a/_sources/autoapi/lasdi/param/index.rst.txt
+++ b/_sources/autoapi/lasdi/param/index.rst.txt
@@ -32,64 +32,167 @@ Functions
 Module Contents
 ---------------
 
-.. py:function:: get_1dspace_from_list(config)
+.. py:function:: get_1dspace_from_list(param_dict: dict) -> tuple[int, numpy.ndarray]
+
+   This function generates the parameter range (set of possible parameter values) for a parameter
+   that uses the list type test space. That is, "test_space_type" should be a key for the
+   parameter dictionary and the corresponding value should be "list". The parameter dictionary
+   should also have a "list" key whose value is a list of the possible parameter values.
+
+   We parse this list and turn it into a numpy ndarray.
+
+
+   -----------------------------------------------------------------------------------------------
+   :Parameters: * **param_dict** (*A dictionary specifying one of the parameters. We should fetch this from the*)
+                * **configuration yaml file. It must have a "list" key whose corresponding value is a list of**
+                * **floats.**
+
+   -----------------------------------------------------------------------------------------------
+   :returns: * **Two arguments** (*Nx and paramRange. paramRange is a 1d numpy ndarray (whose ith value is the*)
+             * *i'th element of param_dict["list"]). Nx is the length of paramRange.*
+
+
+.. py:function:: create_uniform_1dspace(param_dict: dict) -> tuple[int, numpy.ndarray]
+
+   This function generates the parameter range (set of possible parameter values) for a parameter
+   that uses the uniform type test space. That is, "test_space_type" should be a key for the
+   parameter dictionary and the corresponding value should be "uniform". The parameter dictionary
+   should also have the following keys:
+       "min"
+       "max"
+       "sample_size"
+       "log_scale"
+   "min" and "max" specify the minimum and maximum value of the parameter, respectively.
+   "sample_size" specifies the number of parameter values we generate. Finally, log_scale, if
+   true, specifies if we should use a uniform or logarithmic spacing between samples of the
+   parameter.
+
+   The values corresponding to "min" and "max" should be floats while the values corresponding to
+   "sample_size" and "log_scale" should be an int and a bool, respectively.
+
+
+   -----------------------------------------------------------------------------------------------
+   :Parameters: * **param_dict** (*A dictionary specifying one of the parameters. We should fetch this from the*)
+                * **configuration yaml file. It must have a "min", "max", "sample_size", and "log_scale"**
+                * **keys (see above).**
+
+   -----------------------------------------------------------------------------------------------
+   :returns: * **Two arguments** (*Nx and paramRange. paramRange is a 1d numpy ndarray (whose ith value is the*)
+             * *i'th possible value of the parameter. Thus, paramRange[0] = param_dict["min"] and*
+             * *paramRange[-1] = param_dict["max"]). Nx is the length of paramRange or, equivalently*
+             * *param_dict["sample_size"].*
 
-.. py:function:: create_uniform_1dspace(config)
 
 .. py:data:: getParam1DSpace
+   :type:  dict[str, callable]
 
-.. py:class:: ParameterSpace(config)
+.. py:class:: ParameterSpace(config: dict)
 
    .. py:attribute:: param_list
+      :type:  list[dict]
       :value: []
 
 
 
    .. py:attribute:: param_name
+      :type:  list[str]
       :value: []
 
 
 
    .. py:attribute:: n_param
+      :type:  int
       :value: 0
 
 
 
    .. py:attribute:: train_space
+      :type:  numpy.ndarray
       :value: None
 
 
 
    .. py:attribute:: test_space
+      :type:  numpy.ndarray
       :value: None
 
 
 
    .. py:attribute:: n_init
+      :type:  int
       :value: 0
 
 
 
    .. py:attribute:: test_grid_sizes
+      :type:  list[int]
       :value: []
 
 
 
    .. py:attribute:: test_meshgrid
+      :type:  tuple[numpy.ndarray]
       :value: None
 
 
 
-   .. py:method:: n_train()
+   .. py:method:: n_train() -> int
+
+      Returns the number of combinations of parameters in the training set.
+
+
+
+   .. py:method:: n_test() -> int
+
+      Returns the number of combinations of parameters in the testing set.
+
+
+
+   .. py:method:: createInitialTrainSpace(param_list: list[dict]) -> numpy.ndarray
+
+      Sets up a grid of parameter values to train at. Note that we only use the min and max value
+      of each parameter when setting up this grid.
+
+
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **param_list** (*A list of parameter dictionaries. Each entry should be a dictionary with the*)
+                   * **following keys** --
+
+                     - name
+                     - min
+                     - max
+
+      -------------------------------------------------------------------------------------------
+      :returns: * *A 2d array of shape ((2)^k, k), where k is the number of parameters (k == len(param_list)).*
+                * *The i'th column is the flattened i'th mesh_grid array we when we create a mesh grid using*
+                * *the min and max value of each parameter as the argument. See "createHyperMeshGrid" for*
+                * *details.*
+                * *Specifically, we return exactly what "createHyperGridSpace" returns. See the doc-string*
+                * *for that function for further details.*
 
 
-   .. py:method:: n_test()
 
+   .. py:method:: createTestGridSpace(param_list: list[dict]) -> tuple[list[int], tuple[numpy.ndarray], numpy.ndarray]
 
-   .. py:method:: createInitialTrainSpace(param_list)
+      This function sets up a grid of parameter values to test at.
 
 
-   .. py:method:: createTestGridSpace(param_list)
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **param_list** (*A list of parameter dictionaries. Each dictionary should either use the*)
+                   * **"uniform" or "list" format. See create_uniform_1dspace and get_1dspace_from_list,**
+                   * **respectively.**
+
+      -------------------------------------------------------------------------------------------
+      :returns: * *A three element tuple.*
+                * *The first is a list whose i'th element specifies the number of distinct values of the i'th*
+                * *parameter we consider (this is the length of the i'th element of "paramRanges" below).*
+                * *The second is a a tuple of k numpy ndarrays (where k = len(param_list)), the i'th one of*
+                * *which is a k-dimensional array with shape (N0, ... , N{k - 1}), where Ni =*
+                * *param_list[i].size whose i(0), ... , i(k - 1) element specifies the value of the i'th*
+                * *parameter in the i(0), ... , i(k - 1)'th unique combination of parameter values.*
+                * *The third one is a 2d array of parameter values. It has shape (M, k), where*
+                * *M = \prod_{i = 0}^{k - 1} param_list[i].size.*
+
 
 
    .. py:method:: getParameter(param_vector)
@@ -99,35 +202,110 @@ Module Contents
 
 
 
-   .. py:method:: createHyperMeshGrid(param_ranges)
+   .. py:method:: createHyperMeshGrid(param_ranges: list[numpy.ndarray]) -> tuple[numpy.ndarray]
+
+      This function generates arrays of parameter values. Specifically, if there are k
+      parameters (param_ranges has k elements), then we return k k-d arrays, the i'th one of
+      which is a k-dimensional array whose i(0), ... , i(k - 1) element specifies the value of
+      the i'th variable in the i(0), ... , i(k - 1)'th unique combination of parameter values.
+
+
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **param_ranges** (*list of numpy 1d arrays, each corresponding to 1d parameter grid space. The*)
+                   * **i'th element of this list should be a 2-element numpy.ndarray object housing the max and**
+                   * **min value for the i'th parameter. The list size should equal the number of parameters.**
+
+      -------------------------------------------------------------------------------------------
+      :returns: * *the "paramSpaces" tuple. This is a tuple of numpy ndarray objects, the i'th one of which*
+                * *gives the grid of parameter values for the i'th parameter. Specifically, if there are*
+                * *k parameters and if param_range[i].size = Ni, then the j'th return array has shape*
+                * *(N0, ... , N{k - 1}) and the i(0), ... , i(k - 1) element of this array houses the i(j)'th*
+                * *value of the j'th parameter.*
+                * *Thus, if there are k parameters, the returned tuple has k elements, each one of*
+                * *which is an array of shape (N0, ... , N{k - 1}).*
+
+
+
+   .. py:method:: createHyperGridSpace(mesh_grids: tuple[numpy.ndarray]) -> numpy.ndarray
+
+      Flattens the mesh_grid numpy.ndarray objects returned by createHyperMeshGrid and combines
+      them into a single 2d array of shape (grid size, number of parameters) (see below).
+
+
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **mesh_grids** (*tuple of numpy nd arrays, corresponding to each parameter. This should ALWAYS*)
+                   * **be the output of the "CreateHyperMeshGrid" function. See the return section of that**
+                   * **function's docstring for details.**
+
+      -------------------------------------------------------------------------------------------
+      :returns: * *The param_grid. This is a 2d numpy.ndarray object of shape (grid size, number of*
+                * *parameters). If each element of mesh_grids is a numpy.ndarray object of shape (N(1), ... ,*
+                * *N(k)) (k parameters), then (grid size) = N(1)*N(2)*...*N(k) and (number of parameters) = k.*
+
+
+
+   .. py:method:: appendTrainSpace(param: numpy.ndarray) -> None
+
+      Adds a new parameter to self's train space attribute.
+
+
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **param** (*A 1d numpy ndarray object. It should have shape (n_param) and should hold a*)
+                   * **parameter value that we want to add to the training set.**
+
+      -------------------------------------------------------------------------------------------
+      :rtype: Nothing!
+
+
+
+   .. py:method:: export() -> dict
+
+      This function packages the testing/training examples into a dictionary, which it returns.
+
 
-      param_ranges: list of numpy 1d arrays, each corresponding to 1d parameter grid space.
-                    The list size is equal to the number of parameters.
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **None!**
+                   * **-------------------------------------------------------------------------------------------**
 
-      Output: paramSpaces
-          - tuple of numpy nd arrays, corresponding to each parameter.
-            Dimension of the array equals to the number of parameters
+      :returns: * *A dictionary with 4 keys. Below is a list of the keys with a short description of each*
+                * *corresponding value.* -- train_space: self.train_space, a 2d array of shape (n_train, n_param) whose i,j element
+                  holds the value of the j'th parameter in the i'th training case.
 
+                  test_space: self.test_space, a 2d array of shape (n_test, n_param) whose i,j element
+                  holds the value of the j'th parameter in the i'th testing case.
 
+                  test_grid_sizes: A list whose i'th element specifies how many distinct parameter values
+                  we use for the i'th parameter.
 
-   .. py:method:: createHyperGridSpace(mesh_grids)
+                  test_meshgrid: a tuple of n_param numpy.ndarray array objects whose i'th element is a
+                  n_param-dimensional array whose i(1), i(2), ... , i(n_param) element holds the value of
+                  the i'th parameter in the i(1), ... , i(n_param) combination of parameter values in the
+                  testing test.
 
-      mesh_grids: tuple of numpy nd arrays, corresponding to each parameter.
-                  Dimension of the array equals to the number of parameters
+                  n_init: The number of combinations of training parameters in the training set.
 
-      Output: param_grid
-          - numpy 2d array of size (grid size x number of parameters).
 
-          grid size is the size of a numpy nd array.
 
+   .. py:method:: load(dict_: dict) -> None
 
+      This function builds a parameter space object from a dictionary. This dictionary should
+      be one that was returned by th export method, or a loaded copy of a dictionary that was
+      returned by the export method.
 
-   .. py:method:: appendTrainSpace(param)
 
+      -------------------------------------------------------------------------------------------
+      :Parameters: * **dict_** (*This should be a dictionary with the following keys:*) --
 
-   .. py:method:: export()
+                     - train_space
+                     - test_space
+                     - test_grid_sizes
+                     - test_meshgrid
+                     - n_init
+                   * **This dictionary should have been returned by the export method. We use the values in this**
+                   * **dictionary to set up self.**
 
+      -------------------------------------------------------------------------------------------
+      :rtype: Nothing!
 
-   .. py:method:: load(dict_)
 
 
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
index 69cf38c..cb633a2 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -18,7 +18,10 @@ It also supports parametric interpolation of latent dynamics according to uncert
 References
 ===================
 
+<<<<<<< HEAD
 * Bonneville, Christophe, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A. Messenger, Siu Wun Cheung et al. "A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling." arXiv preprint arXiv:2403.10748 (2024).
+=======
+>>>>>>> 9269e9cb2d85c993efecb52c726f2f1ff657d487
 * Fries, William D., Xiaolong He, and Youngsoo Choi. "LaSDI: Parametric latent space dynamics identification." Computer Methods in Applied Mechanics and Engineering 399 (2022): 115436.
 * He, Xiaolong, Youngsoo Choi, William D. Fries, Jonathan L. Belof, and Jiun-Shyan Chen. "gLaSDI: Parametric physics-informed greedy latent space dynamics identification." Journal of Computational Physics 489 (2023): 112267.
 * Tran, April, Xiaolong He, Daniel A. Messenger, Youngsoo Choi, and David M. Bortz. "Weak-form latent space dynamics identification." Computer Methods in Applied Mechanics and Engineering 427 (2024): 116998.
diff --git a/autoapi/lasdi/gp/index.html b/autoapi/lasdi/gp/index.html
index ff468ef..7a4efa3 100644
--- a/autoapi/lasdi/gp/index.html
+++ b/autoapi/lasdi/gp/index.html
@@ -100,14 +100,14 @@
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this heading"></a></h2>
 <table class="autosummary longtable docutils align-default">
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.gp.fit_gps" title="lasdi.gp.fit_gps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_gps</span></code></a>(X, Y)</p></td>
-<td><p>Trains each GP given the interpolation dataset.</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.gp.fit_gps" title="lasdi.gp.fit_gps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_gps</span></code></a>(...)</p></td>
+<td><p>Trains a GP for each column of Y. If Y has shape N x k, then we train k GP regressors. In this</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#lasdi.gp.eval_gp" title="lasdi.gp.eval_gp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_gp</span></code></a>(gp_dictionnary, param_grid)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#lasdi.gp.eval_gp" title="lasdi.gp.eval_gp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_gp</span></code></a>(→ tuple)</p></td>
 <td><p>Computes the GPs predictive mean and standard deviation for points of the parameter space grid</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.gp.sample_coefs" title="lasdi.gp.sample_coefs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_coefs</span></code></a>(gp_dictionnary, param, n_samples)</p></td>
-<td><p>Generates sample sets of ODEs for one given parameter.</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.gp.sample_coefs" title="lasdi.gp.sample_coefs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_coefs</span></code></a>(gp_list, param, n_samples)</p></td>
+<td><p>Generates sets of ODE (SINDy) coefficients sampled from the predictive distribution for those</p></td>
 </tr>
 </tbody>
 </table>
@@ -116,26 +116,71 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 <h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="lasdi.gp.fit_gps">
-<span class="sig-prename descclassname"><span class="pre">lasdi.gp.</span></span><span class="sig-name descname"><span class="pre">fit_gps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.gp.fit_gps" title="Permalink to this definition"></a></dt>
-<dd><p>Trains each GP given the interpolation dataset.
-X: (n_train, n_param) numpy 2d array
-Y: (n_train, n_coef) numpy 2d array
-We assume each target coefficient is independent with each other.
-gp_dictionnary is a dataset containing the trained GPs (as sklearn objects)</p>
+<span class="sig-prename descclassname"><span class="pre">lasdi.gp.</span></span><span class="sig-name descname"><span class="pre">fit_gps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">sklearn.gaussian_process.GaussianProcessRegressor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#lasdi.gp.fit_gps" title="Permalink to this definition"></a></dt>
+<dd><p>Trains a GP for each column of Y. If Y has shape N x k, then we train k GP regressors. In this
+case, we assume that X has shape N x M. Thus, the Input to the GP is in mathbb{R}^M. For each
+k, we train a GP where the i’th row of X is the input and the i,k component of Y is the
+corresponding target. Thus, we return a list of k GP Regressor objects, the k’th one of which
+makes predictions for the k’th coefficient in the latent dynamics.</p>
+<p>We assume each target coefficient is independent with each other.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>parameters)</strong></p></li>
+<li><p><strong>Y</strong> (<em>A 2d numpy array of shape (n_train, n_coef), where n_train is the number of training</em>)</p></li>
+<li><p><strong>examples and n_coef is the number of coefficients in the latent dynamics.</strong></p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>columns of X and whose corresponding target values are the elements of the i’th column of Y.</em></p></li>
+</ul>
+</div></blockquote>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="lasdi.gp.eval_gp">
-<span class="sig-prename descclassname"><span class="pre">lasdi.gp.</span></span><span class="sig-name descname"><span class="pre">eval_gp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gp_dictionnary</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.gp.eval_gp" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">lasdi.gp.</span></span><span class="sig-name descname"><span class="pre">eval_gp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gp_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">sklearn.gaussian_process.GaussianProcessRegressor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span></span></span><a class="headerlink" href="#lasdi.gp.eval_gp" title="Permalink to this definition"></a></dt>
 <dd><p>Computes the GPs predictive mean and standard deviation for points of the parameter space grid</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>object that predicts the i’th coefficient.</strong></p></li>
+<li><p><strong>param_grid</strong> (<em>A 2d numpy.ndarray object of shape (number of parameter combination, number of</em>)</p></li>
+<li><p><strong>parameters). The i,j element of this array specifies the value of the j’th parameter in the</strong></p></li>
+<li><p><strong>i’th combination of parameters. We use this as the testing set for the GP evaluation.</strong></p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>at each training example, respectively.</em></p></li>
+<li><p><em>Thus, the i,j element of the first return variable holds the predicted mean of the j’th</em></p></li>
+<li><p><em>coefficient in the latent dynamics at the i’th training example. Likewise, the i,j element of</em></p></li>
+<li><p><em>the second return variable holds the standard deviation in the predicted distribution for the</em></p></li>
+<li><p><em>j’th coefficient in the latent dynamics at the i’th combination of parameter values.</em></p></li>
+</ul>
+</div></blockquote>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="lasdi.gp.sample_coefs">
-<span class="sig-prename descclassname"><span class="pre">lasdi.gp.</span></span><span class="sig-name descname"><span class="pre">sample_coefs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gp_dictionnary</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.gp.sample_coefs" title="Permalink to this definition"></a></dt>
-<dd><p>Generates sample sets of ODEs for one given parameter.
-coef_samples is a list of length n_samples, where each terms is a matrix of SINDy coefficients sampled from the GP predictive
-distributions</p>
+<span class="sig-prename descclassname"><span class="pre">lasdi.gp.</span></span><span class="sig-name descname"><span class="pre">sample_coefs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gp_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">sklearn.gaussian_process.GaussianProcessRegressor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.gp.sample_coefs" title="Permalink to this definition"></a></dt>
+<dd><p>Generates sets of ODE (SINDy) coefficients sampled from the predictive distribution for those
+coefficients at the specified parameter value (parma). Specifically, for the k’th SINDy
+coefficient, we draw n_samples samples of the predictive distribution for the k’th coefficient
+when param is the parameter.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>object that predicts the i’th coefficient.</strong></p></li>
+<li><p><strong>param</strong> (<em>A combination of parameter values. i.e., a single test example. We evaluate each GP in</em>)</p></li>
+<li><p><strong>the gp_list at this parameter value (getting a prediction for each coefficient).</strong></p></li>
+<li><p><strong>n_samples</strong> (<em>Number of samples of the predicted latent dynamics used to build ensemble of fom</em>)</p></li>
+<li><p><strong>predictions. N_s in the paper.</strong></p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>sample of the j’th SINDy coefficient.</em></p></li>
+</ul>
+</div></blockquote>
 </dd></dl>
 
 </section>
diff --git a/autoapi/lasdi/inputs/index.html b/autoapi/lasdi/inputs/index.html
index 81425e7..20ee2c3 100644
--- a/autoapi/lasdi/inputs/index.html
+++ b/autoapi/lasdi/inputs/index.html
@@ -111,17 +111,7 @@ <h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headin
 <table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.inputs.InputParser" title="lasdi.inputs.InputParser"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InputParser</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="functions">
-<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this heading"></a></h2>
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.inputs.getDictFromList" title="lasdi.inputs.getDictFromList"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getDictFromList</span></code></a>(list_, inputDict)</p></td>
-<td><p>get a dict with {key: val} from a list of dicts</p></td>
+<td><p>A InputParser objects acts as a wrapper around a dictionary of settings. Thus, each setting is</p></td>
 </tr>
 </tbody>
 </table>
@@ -130,39 +120,61 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 <h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading"></a></h2>
 <dl class="py data">
 <dt class="sig sig-object py" id="lasdi.inputs.verbose">
-<span class="sig-prename descclassname"><span class="pre">lasdi.inputs.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#lasdi.inputs.verbose" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">lasdi.inputs.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#lasdi.inputs.verbose" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="lasdi.inputs.InputParser">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.inputs.</span></span><span class="sig-name descname"><span class="pre">InputParser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.inputs.InputParser" title="Permalink to this definition"></a></dt>
-<dd><dl class="py attribute">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.inputs.</span></span><span class="sig-name descname"><span class="pre">InputParser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">InputParser.__init__.dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.inputs.InputParser" title="Permalink to this definition"></a></dt>
+<dd><p>A InputParser objects acts as a wrapper around a dictionary of settings. Thus, each setting is
+a key and the corresponding value is the setting’s value. Because one setting may itself be
+a dictionary (we often group settings; each group has a name but several constituent settings),
+the underlying dictionary is structured as a sequence of nested dictionaries. This class allows
+the user to select a specific setting from that structure by specifying (via a list of strings)
+where in that nested structure the desired setting lives.</p>
+<dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.inputs.InputParser.dict_">
-<span class="sig-name descname"><span class="pre">dict_</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.inputs.InputParser.dict_" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dict_</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.inputs.InputParser.dict_" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.inputs.InputParser.name">
-<span class="sig-name descname"><span class="pre">name</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">''</span></em><a class="headerlink" href="#lasdi.inputs.InputParser.name" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">name</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">''</span></em><a class="headerlink" href="#lasdi.inputs.InputParser.name" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.inputs.InputParser.getInput">
-<span class="sig-name descname"><span class="pre">getInput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datatype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.inputs.InputParser.getInput" title="Permalink to this definition"></a></dt>
-<dd><p>Find the value corresponding to the list of keys.
-If the specified keys do not exist, use the fallback value.
-If the fallback value does not exist, returns an error.
-If the datatype is specified, enforce the output value has the right datatype.</p>
-</dd></dl>
-
+<span class="sig-name descname"><span class="pre">getInput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datatype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.inputs.InputParser.getInput" title="Permalink to this definition"></a></dt>
+<dd><p>A InputParser object acts as a wrapper around a dictionary of settings. That is, <a href="#id1"><span class="problematic" id="id2">self.dict_</span></a>
+is structured as a nested family of dictionaries. Each setting corresponds to a key in
+<a href="#id3"><span class="problematic" id="id4">self.dict_</span></a>. The setting’s value is the corresponding value in <a href="#id5"><span class="problematic" id="id6">self.dict_</span></a>. In many cases,
+a particular setting may be nested within others. That is, a setting’s value may itself be
+another dictionary housing various sub-settings. This function allows us to fetch a
+specific setting from this nested structure.</p>
+<p>Specifically, we specify a list of strings. keys[0] should be a key in <a href="#id7"><span class="problematic" id="id8">self.dict_</span></a>
+If so, we set val = self.dict_[keys[0]]. If there are more keys, then val should be a
+dictionary and keys[1] should be a key in this dictionary. In this case, we replace val
+with val[key[1]] and so on. This continues until we have exhausted all keys. There is one
+important exception:</p>
+<blockquote>
+<div><p>If at some point in the process, there are more keys but val is not a dictionary, or if
+there are more keys and val is a dictionary but the next key is not a key in that
+dictionary, then we return the fallback value. If the fallback value does not exist,
+returns an error.</p>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>dictionary and keys[1] should be a key in this dictionary. In this case, we replace val</strong></p></li>
+<li><p><strong>with val[key[1]] and so on. This continues until we have exhausted all keys.</strong></p></li>
+<li><p><strong>fallback</strong> (<em>A sort of default value. If at some point, val is not a dictionary (and there are</em>)</p></li>
+<li><p><strong>more keys) or val is a dictionary but the next key is not a valid key in that dictionary,</strong></p></li>
+<li><p><strong>then we return the fallback value.</strong></p></li>
+<li><p><strong>datatype</strong> (<em>If not None, then we require that the final val has this datatype. If the final</em>)</p></li>
+<li><p><strong>val does not have the desired datatype, we raise an exception.</strong></p></li>
+</ul>
+</div></blockquote>
 </dd></dl>
 
-<dl class="py function">
-<dt class="sig sig-object py" id="lasdi.inputs.getDictFromList">
-<span class="sig-prename descclassname"><span class="pre">lasdi.inputs.</span></span><span class="sig-name descname"><span class="pre">getDictFromList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputDict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.inputs.getDictFromList" title="Permalink to this definition"></a></dt>
-<dd><p>get a dict with {key: val} from a list of dicts
-NOTE: it returns only the first item in the list,
-even if the list has more than one dict with {key: val}.</p>
 </dd></dl>
 
 </section>
diff --git a/autoapi/lasdi/latent_space/index.html b/autoapi/lasdi/latent_space/index.html
index 86268c0..6fa0192 100644
--- a/autoapi/lasdi/latent_space/index.html
+++ b/autoapi/lasdi/latent_space/index.html
@@ -123,8 +123,8 @@ <h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headin
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this heading"></a></h2>
 <table class="autosummary longtable docutils align-default">
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.latent_space.initial_condition_latent" title="lasdi.latent_space.initial_condition_latent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initial_condition_latent</span></code></a>(param_grid, physics, autoencoder)</p></td>
-<td><p>Outputs the initial condition in the latent space: Z0 = encoder(U0)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.latent_space.initial_condition_latent" title="lasdi.latent_space.initial_condition_latent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initial_condition_latent</span></code></a>(→ list[numpy.ndarray])</p></td>
+<td><p>This function maps a set of initial conditions for the fom to initial conditions for the</p></td>
 </tr>
 </tbody>
 </table>
@@ -138,83 +138,100 @@ <h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalin
 
 <dl class="py function">
 <dt class="sig sig-object py" id="lasdi.latent_space.initial_condition_latent">
-<span class="sig-prename descclassname"><span class="pre">lasdi.latent_space.</span></span><span class="sig-name descname"><span class="pre">initial_condition_latent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">physics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autoencoder</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.initial_condition_latent" title="Permalink to this definition"></a></dt>
-<dd><p>Outputs the initial condition in the latent space: Z0 = encoder(U0)</p>
+<span class="sig-prename descclassname"><span class="pre">lasdi.latent_space.</span></span><span class="sig-name descname"><span class="pre">initial_condition_latent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">physics</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../physics/index.html#lasdi.physics.Physics" title="lasdi.physics.Physics"><span class="pre">lasdi.physics.Physics</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">autoencoder</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.nn.Module</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#lasdi.latent_space.initial_condition_latent" title="Permalink to this definition"></a></dt>
+<dd><p>This function maps a set of initial conditions for the fom to initial conditions for the
+latent space dynamics. Specifically, we take in a set of possible parameter values. For each
+set of parameter values, we recover the fom IC (from physics), then map this fom IC to a
+latent space IC (by encoding it using the autoencoder). We do this for each parameter
+combination and then return a list housing the latent space ICs.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>physics</strong> (<em>A “Physics” object that stores the datasets for each parameter combination.</em>)</p></li>
+<li><p><strong>autoencoder</strong> (<em>The actual autoencoder object that we use to map the ICs into the latent space.</em>)</p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>the i’th set of parameters, then the i’th element of the returned list is Z0_i = encoder(U0_i).</em></p></li>
+</ul>
+</div></blockquote>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.latent_space.</span></span><span class="sig-name descname"><span class="pre">MultiLayerPerceptron</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">act_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sigmoid'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshape_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshape_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.latent_space.</span></span><span class="sig-name descname"><span class="pre">MultiLayerPerceptron</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer_sizes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">act_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'sigmoid'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshape_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshape_shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.n_layers">
-<span class="sig-name descname"><span class="pre">n_layers</span></span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.n_layers" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">n_layers</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.n_layers" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.layer_sizes">
-<span class="sig-name descname"><span class="pre">layer_sizes</span></span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.layer_sizes" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">layer_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.layer_sizes" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.fcs">
-<span class="sig-name descname"><span class="pre">fcs</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.fcs" title="Permalink to this definition"></a></dt>
+<dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.layers">
+<span class="sig-name descname"><span class="pre">layers</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.nn.Module</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.layers" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.reshape_index">
-<span class="sig-name descname"><span class="pre">reshape_index</span></span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.reshape_index" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">reshape_index</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.reshape_index" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.reshape_shape">
-<span class="sig-name descname"><span class="pre">reshape_shape</span></span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.reshape_shape" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">reshape_shape</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.reshape_shape" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.act_type">
-<span class="sig-name descname"><span class="pre">act_type</span></span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.act_type" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.use_multihead">
-<span class="sig-name descname"><span class="pre">use_multihead</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.use_multihead" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">act_type</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.act_type" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.forward" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.apply_attention">
-<span class="sig-name descname"><span class="pre">apply_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">act_idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.apply_attention" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.forward" title="Permalink to this definition"></a></dt>
+<dd><p>This function defines the forward pass through self.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>elements, then the final k elements of x’s shape must match self.reshape_shape.</strong></p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>of its shape match those of self.reshape_shape.</em></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.latent_space.MultiLayerPerceptron.init_weight">
-<span class="sig-name descname"><span class="pre">init_weight</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.init_weight" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">init_weight</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#lasdi.latent_space.MultiLayerPerceptron.init_weight" title="Permalink to this definition"></a></dt>
+<dd><p>This function initializes the weight matrices and bias vectors in self’s layers.</p>
+</dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="lasdi.latent_space.Autoencoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.latent_space.</span></span><span class="sig-name descname"><span class="pre">Autoencoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">physics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.Autoencoder" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.latent_space.</span></span><span class="sig-name descname"><span class="pre">Autoencoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">physics</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../physics/index.html#lasdi.physics.Physics" title="lasdi.physics.Physics"><span class="pre">lasdi.physics.Physics</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.Autoencoder" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.Autoencoder.qgrid_size">
-<span class="sig-name descname"><span class="pre">qgrid_size</span></span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.qgrid_size" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">qgrid_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#lasdi.latent_space.Autoencoder.qgrid_size" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.Autoencoder.space_dim">
-<span class="sig-name descname"><span class="pre">space_dim</span></span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.space_dim" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">space_dim</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span></em><a class="headerlink" href="#lasdi.latent_space.Autoencoder.space_dim" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.latent_space.Autoencoder.n_z">
-<span class="sig-name descname"><span class="pre">n_z</span></span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.n_z" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">n_z</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#lasdi.latent_space.Autoencoder.n_z" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -229,18 +246,26 @@ <h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.latent_space.Autoencoder.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.forward" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.forward" title="Permalink to this definition"></a></dt>
+<dd><p>This function defines the forward pass through self.</p>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.latent_space.Autoencoder.export">
-<span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.export" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">dict</span></span></span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.export" title="Permalink to this definition"></a></dt>
+<dd><p>This function extracts self’s parameters and returns them in a dictionary.</p>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.latent_space.Autoencoder.load">
-<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.load" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#lasdi.latent_space.Autoencoder.load" title="Permalink to this definition"></a></dt>
+<dd><p>This function loads self’s state dictionary.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>layer sizes) as self.</strong></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
 
 </dd></dl>
 
diff --git a/autoapi/lasdi/param/index.html b/autoapi/lasdi/param/index.html
index f88ae1e..6485170 100644
--- a/autoapi/lasdi/param/index.html
+++ b/autoapi/lasdi/param/index.html
@@ -120,11 +120,11 @@ <h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headin
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this heading"></a></h2>
 <table class="autosummary longtable docutils align-default">
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.param.get_1dspace_from_list" title="lasdi.param.get_1dspace_from_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_1dspace_from_list</span></code></a>(config)</p></td>
-<td><p></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#lasdi.param.get_1dspace_from_list" title="lasdi.param.get_1dspace_from_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_1dspace_from_list</span></code></a>(→ tuple[int, numpy.ndarray])</p></td>
+<td><p>This function generates the parameter range (set of possible parameter values) for a parameter</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#lasdi.param.create_uniform_1dspace" title="lasdi.param.create_uniform_1dspace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_uniform_1dspace</span></code></a>(config)</p></td>
-<td><p></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#lasdi.param.create_uniform_1dspace" title="lasdi.param.create_uniform_1dspace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_uniform_1dspace</span></code></a>(→ tuple[int, numpy.ndarray])</p></td>
+<td><p>This function generates the parameter range (set of possible parameter values) for a parameter</p></td>
 </tr>
 </tbody>
 </table>
@@ -133,81 +133,154 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 <h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="lasdi.param.get_1dspace_from_list">
-<span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">get_1dspace_from_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.get_1dspace_from_list" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">get_1dspace_from_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#lasdi.param.get_1dspace_from_list" title="Permalink to this definition"></a></dt>
+<dd><p>This function generates the parameter range (set of possible parameter values) for a parameter
+that uses the list type test space. That is, “test_space_type” should be a key for the
+parameter dictionary and the corresponding value should be “list”. The parameter dictionary
+should also have a “list” key whose value is a list of the possible parameter values.</p>
+<p>We parse this list and turn it into a numpy ndarray.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>floats.</strong></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="lasdi.param.create_uniform_1dspace">
-<span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">create_uniform_1dspace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.create_uniform_1dspace" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">create_uniform_1dspace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#lasdi.param.create_uniform_1dspace" title="Permalink to this definition"></a></dt>
+<dd><p>This function generates the parameter range (set of possible parameter values) for a parameter
+that uses the uniform type test space. That is, “test_space_type” should be a key for the
+parameter dictionary and the corresponding value should be “uniform”. The parameter dictionary
+should also have the following keys:</p>
+<blockquote>
+<div><p>“min”
+“max”
+“sample_size”
+“log_scale”</p>
+</div></blockquote>
+<p>“min” and “max” specify the minimum and maximum value of the parameter, respectively.
+“sample_size” specifies the number of parameter values we generate. Finally, log_scale, if
+true, specifies if we should use a uniform or logarithmic spacing between samples of the
+parameter.</p>
+<p>The values corresponding to “min” and “max” should be floats while the values corresponding to
+“sample_size” and “log_scale” should be an int and a bool, respectively.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>keys (see above).</strong></p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>paramRange[-1] = param_dict[“max”]). Nx is the length of paramRange or, equivalently</em></p></li>
+<li><p><em>param_dict[“sample_size”].</em></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
 
 <dl class="py data">
 <dt class="sig sig-object py" id="lasdi.param.getParam1DSpace">
-<span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">getParam1DSpace</span></span><a class="headerlink" href="#lasdi.param.getParam1DSpace" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">getParam1DSpace</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">callable</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#lasdi.param.getParam1DSpace" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">ParameterSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lasdi.param.</span></span><span class="sig-name descname"><span class="pre">ParameterSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace" title="Permalink to this definition"></a></dt>
 <dd><dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.param_list">
-<span class="sig-name descname"><span class="pre">param_list</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.param_list" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">param_list</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.param_list" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.param_name">
-<span class="sig-name descname"><span class="pre">param_name</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.param_name" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">param_name</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.param_name" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.n_param">
-<span class="sig-name descname"><span class="pre">n_param</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.n_param" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">n_param</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.n_param" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.train_space">
-<span class="sig-name descname"><span class="pre">train_space</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.train_space" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">train_space</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.train_space" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.test_space">
-<span class="sig-name descname"><span class="pre">test_space</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.test_space" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">test_space</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.test_space" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.n_init">
-<span class="sig-name descname"><span class="pre">n_init</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.n_init" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">n_init</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.n_init" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.test_grid_sizes">
-<span class="sig-name descname"><span class="pre">test_grid_sizes</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.test_grid_sizes" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">test_grid_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[]</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.test_grid_sizes" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.test_meshgrid">
-<span class="sig-name descname"><span class="pre">test_meshgrid</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.test_meshgrid" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">test_meshgrid</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#lasdi.param.ParameterSpace.test_meshgrid" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.n_train">
-<span class="sig-name descname"><span class="pre">n_train</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.n_train" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">n_train</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">int</span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.n_train" title="Permalink to this definition"></a></dt>
+<dd><p>Returns the number of combinations of parameters in the training set.</p>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.n_test">
-<span class="sig-name descname"><span class="pre">n_test</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.n_test" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">n_test</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">int</span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.n_test" title="Permalink to this definition"></a></dt>
+<dd><p>Returns the number of combinations of parameters in the testing set.</p>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.createInitialTrainSpace">
-<span class="sig-name descname"><span class="pre">createInitialTrainSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_list</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.createInitialTrainSpace" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">createInitialTrainSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.createInitialTrainSpace" title="Permalink to this definition"></a></dt>
+<dd><p>Sets up a grid of parameter values to train at. Note that we only use the min and max value
+of each parameter when setting up this grid.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>name</p></li>
+<li><p>min</p></li>
+<li><p>max</p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>the min and max value of each parameter as the argument. See “createHyperMeshGrid” for</em></p></li>
+<li><p><em>details.</em></p></li>
+<li><p><em>Specifically, we return exactly what “createHyperGridSpace” returns. See the doc-string</em></p></li>
+<li><p><em>for that function for further details.</em></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.createTestGridSpace">
-<span class="sig-name descname"><span class="pre">createTestGridSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_list</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.createTestGridSpace" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">createTestGridSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.createTestGridSpace" title="Permalink to this definition"></a></dt>
+<dd><p>This function sets up a grid of parameter values to test at.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>respectively.</strong></p></li>
+</ul>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>parameter we consider (this is the length of the i’th element of “paramRanges” below).</em></p></li>
+<li><p><em>The second is a a tuple of k numpy ndarrays (where k = len(param_list)), the i’th one of</em></p></li>
+<li><p><em>which is a k-dimensional array with shape (N0, … , N{k - 1}), where Ni =</em></p></li>
+<li><p><em>param_list[i].size whose i(0), … , i(k - 1) element specifies the value of the i’th</em></p></li>
+<li><p><em>parameter in the i(0), … , i(k - 1)’th unique combination of parameter values.</em></p></li>
+<li><p><em>The third one is a 2d array of parameter values. It has shape (M, k), where</em></p></li>
+<li><p><em>M = prod_{i = 0}^{k - 1} param_list[i].size.</em></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.getParameter">
@@ -218,46 +291,98 @@ <h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.createHyperMeshGrid">
-<span class="sig-name descname"><span class="pre">createHyperMeshGrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_ranges</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.createHyperMeshGrid" title="Permalink to this definition"></a></dt>
-<dd><dl class="simple">
-<dt>param_ranges: list of numpy 1d arrays, each corresponding to 1d parameter grid space.</dt><dd><p>The list size is equal to the number of parameters.</p>
-</dd>
-<dt>Output: paramSpaces</dt><dd><ul class="simple">
-<li><p>tuple of numpy nd arrays, corresponding to each parameter.
-Dimension of the array equals to the number of parameters</p></li>
+<span class="sig-name descname"><span class="pre">createHyperMeshGrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_ranges</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.createHyperMeshGrid" title="Permalink to this definition"></a></dt>
+<dd><p>This function generates arrays of parameter values. Specifically, if there are k
+parameters (param_ranges has k elements), then we return k k-d arrays, the i’th one of
+which is a k-dimensional array whose i(0), … , i(k - 1) element specifies the value of
+the i’th variable in the i(0), … , i(k - 1)’th unique combination of parameter values.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>min value for the i’th parameter. The list size should equal the number of parameters.</strong></p></li>
 </ul>
-</dd>
-</dl>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>k parameters and if param_range[i].size = Ni, then the j’th return array has shape</em></p></li>
+<li><p><em>(N0, … , N{k - 1}) and the i(0), … , i(k - 1) element of this array houses the i(j)’th</em></p></li>
+<li><p><em>value of the j’th parameter.</em></p></li>
+<li><p><em>Thus, if there are k parameters, the returned tuple has k elements, each one of</em></p></li>
+<li><p><em>which is an array of shape (N0, … , N{k - 1}).</em></p></li>
+</ul>
+</div></blockquote>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.createHyperGridSpace">
-<span class="sig-name descname"><span class="pre">createHyperGridSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_grids</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.createHyperGridSpace" title="Permalink to this definition"></a></dt>
-<dd><dl>
-<dt>mesh_grids: tuple of numpy nd arrays, corresponding to each parameter.</dt><dd><p>Dimension of the array equals to the number of parameters</p>
-</dd>
-<dt>Output: param_grid</dt><dd><ul class="simple">
-<li><p>numpy 2d array of size (grid size x number of parameters).</p></li>
+<span class="sig-name descname"><span class="pre">createHyperGridSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_grids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.createHyperGridSpace" title="Permalink to this definition"></a></dt>
+<dd><p>Flattens the mesh_grid numpy.ndarray objects returned by createHyperMeshGrid and combines
+them into a single 2d array of shape (grid size, number of parameters) (see below).</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>function’s docstring for details.</strong></p></li>
 </ul>
-<p>grid size is the size of a numpy nd array.</p>
-</dd>
-</dl>
+</div></blockquote>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>N(k)) (k parameters), then (grid size) = N(1)*N(2)</em>…*N(k) and (number of parameters) = k.*</p></li>
+</ul>
+</div></blockquote>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.appendTrainSpace">
-<span class="sig-name descname"><span class="pre">appendTrainSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.appendTrainSpace" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">appendTrainSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.appendTrainSpace" title="Permalink to this definition"></a></dt>
+<dd><p>Adds a new parameter to self’s train space attribute.</p>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.export">
-<span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.export" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">dict</span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.export" title="Permalink to this definition"></a></dt>
+<dd><p>This function packages the testing/training examples into a dictionary, which it returns.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul>
+<li><p><em>A dictionary with 4 keys. Below is a list of the keys with a short description of each</em></p></li>
+<li><p><em>corresponding value.</em> – train_space: self.train_space, a 2d array of shape (n_train, n_param) whose i,j element
+holds the value of the j’th parameter in the i’th training case.</p>
+<p>test_space: self.test_space, a 2d array of shape (n_test, n_param) whose i,j element
+holds the value of the j’th parameter in the i’th testing case.</p>
+<p>test_grid_sizes: A list whose i’th element specifies how many distinct parameter values
+we use for the i’th parameter.</p>
+<p>test_meshgrid: a tuple of n_param numpy.ndarray array objects whose i’th element is a
+n_param-dimensional array whose i(1), i(2), … , i(n_param) element holds the value of
+the i’th parameter in the i(1), … , i(n_param) combination of parameter values in the
+testing test.</p>
+<p>n_init: The number of combinations of training parameters in the training set.</p>
+</li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="lasdi.param.ParameterSpace.load">
-<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lasdi.param.ParameterSpace.load" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#lasdi.param.ParameterSpace.load" title="Permalink to this definition"></a></dt>
+<dd><p>This function builds a parameter space object from a dictionary. This dictionary should
+be one that was returned by th export method, or a loaded copy of a dictionary that was
+returned by the export method.</p>
+<blockquote>
+<div><blockquote>
+<div><ul class="simple">
+<li><p>train_space</p></li>
+<li><p>test_space</p></li>
+<li><p>test_grid_sizes</p></li>
+<li><p>test_meshgrid</p></li>
+<li><p>n_init</p></li>
+</ul>
+</div></blockquote>
+<ul class="simple">
+<li><p><strong>This dictionary should have been returned by the export method. We use the values in this</strong></p></li>
+<li><p><strong>dictionary to set up self.</strong></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
 
 </dd></dl>
 
diff --git a/genindex.html b/genindex.html
index 226eb2e..565a6df 100644
--- a/genindex.html
+++ b/genindex.html
@@ -105,12 +105,10 @@ <h2 id="A">A</h2>
       <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.act_dict">act_dict (in module lasdi.latent_space)</a>
 </li>
       <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.MultiLayerPerceptron.act_type">act_type (lasdi.latent_space.MultiLayerPerceptron attribute)</a>
-</li>
-      <li><a href="autoapi/lasdi/param/index.html#lasdi.param.ParameterSpace.appendTrainSpace">appendTrainSpace() (lasdi.param.ParameterSpace method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.MultiLayerPerceptron.apply_attention">apply_attention() (lasdi.latent_space.MultiLayerPerceptron method)</a>
+      <li><a href="autoapi/lasdi/param/index.html#lasdi.param.ParameterSpace.appendTrainSpace">appendTrainSpace() (lasdi.param.ParameterSpace method)</a>
 </li>
       <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.Autoencoder">Autoencoder (class in lasdi.latent_space)</a>
 </li>
@@ -251,17 +249,15 @@ <h2 id="F">F</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="autoapi/lasdi/enums/index.html#lasdi.enums.Result.Fail">Fail (lasdi.enums.Result attribute)</a>
-</li>
-      <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.MultiLayerPerceptron.fcs">fcs (lasdi.latent_space.MultiLayerPerceptron attribute)</a>
 </li>
       <li><a href="autoapi/lasdi/latent_dynamics/sindy/index.html#lasdi.latent_dynamics.sindy.SINDy.fd">fd (lasdi.latent_dynamics.sindy.SINDy attribute)</a>
 </li>
       <li><a href="autoapi/lasdi/latent_dynamics/sindy/index.html#lasdi.latent_dynamics.sindy.SINDy.fd_oper">fd_oper (lasdi.latent_dynamics.sindy.SINDy attribute)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="autoapi/lasdi/latent_dynamics/sindy/index.html#lasdi.latent_dynamics.sindy.SINDy.fd_type">fd_type (lasdi.latent_dynamics.sindy.SINDy attribute)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="autoapi/lasdi/fd/index.html#lasdi.fd.FDdict">FDdict (in module lasdi.fd)</a>
 </li>
       <li><a href="autoapi/lasdi/gp/index.html#lasdi.gp.fit_gps">fit_gps() (in module lasdi.gp)</a>
@@ -290,12 +286,10 @@ <h2 id="G">G</h2>
 </li>
       <li><a href="autoapi/lasdi/gplasdi/index.html#lasdi.gplasdi.BayesianGLaSDI.get_new_sample_point">get_new_sample_point() (lasdi.gplasdi.BayesianGLaSDI method)</a>
 </li>
-      <li><a href="autoapi/lasdi/inputs/index.html#lasdi.inputs.getDictFromList">getDictFromList() (in module lasdi.inputs)</a>
+      <li><a href="autoapi/lasdi/inputs/index.html#lasdi.inputs.InputParser.getInput">getInput() (lasdi.inputs.InputParser method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="autoapi/lasdi/inputs/index.html#lasdi.inputs.InputParser.getInput">getInput() (lasdi.inputs.InputParser method)</a>
-</li>
       <li><a href="autoapi/lasdi/fd/index.html#lasdi.fd.Stencil.getOperators">getOperators() (lasdi.fd.Stencil method)</a>
 </li>
       <li><a href="autoapi/lasdi/param/index.html#lasdi.param.getParam1DSpace">getParam1DSpace (in module lasdi.param)</a>
@@ -496,6 +490,8 @@ <h2 id="L">L</h2>
       <li><a href="autoapi/lasdi/latent_dynamics/index.html#lasdi.latent_dynamics.LatentDynamics">LatentDynamics (class in lasdi.latent_dynamics)</a>
 </li>
       <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.MultiLayerPerceptron.layer_sizes">layer_sizes (lasdi.latent_space.MultiLayerPerceptron attribute)</a>
+</li>
+      <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.MultiLayerPerceptron.layers">layers (lasdi.latent_space.MultiLayerPerceptron attribute)</a>
 </li>
       <li><a href="autoapi/lasdi/workflow/index.html#lasdi.workflow.ld_dict">ld_dict (in module lasdi.workflow)</a>
 </li>
@@ -885,10 +881,6 @@ <h2 id="U">U</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="autoapi/lasdi/enums/index.html#lasdi.enums.Result.Unexecuted">Unexecuted (lasdi.enums.Result attribute)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="autoapi/lasdi/latent_space/index.html#lasdi.latent_space.MultiLayerPerceptron.use_multihead">use_multihead (lasdi.latent_space.MultiLayerPerceptron attribute)</a>
 </li>
   </ul></td>
 </tr></table>
diff --git a/index.html b/index.html
index 9380768..4bcb0f5 100644
--- a/index.html
+++ b/index.html
@@ -89,15 +89,16 @@ <h1>LaSDI documentation<a class="headerlink" href="#lasdi-documentation" title="
 </section>
 <section id="references">
 <h1>References<a class="headerlink" href="#references" title="Permalink to this heading"></a></h1>
-<ul class="simple">
-<li><p>Bonneville, Christophe, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A. Messenger, Siu Wun Cheung et al. “A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling.” arXiv preprint arXiv:2403.10748 (2024).</p></li>
-<li><p>Fries, William D., Xiaolong He, and Youngsoo Choi. “LaSDI: Parametric latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 399 (2022): 115436.</p></li>
-<li><p>He, Xiaolong, Youngsoo Choi, William D. Fries, Jonathan L. Belof, and Jiun-Shyan Chen. “gLaSDI: Parametric physics-informed greedy latent space dynamics identification.” Journal of Computational Physics 489 (2023): 112267.</p></li>
-<li><p>Tran, April, Xiaolong He, Daniel A. Messenger, Youngsoo Choi, and David M. Bortz. “Weak-form latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 427 (2024): 116998.</p></li>
-<li><p>Park, Jun Sur Richard, Siu Wun Cheung, Youngsoo Choi, and Yeonjong Shin. “tLaSDI: Thermodynamics-informed latent space dynamics identification.” arXiv preprint arXiv:2403.05848 (2024).</p></li>
-<li><p>Bonneville, Christophe, Youngsoo Choi, Debojyoti Ghosh, and Jonathan L. Belof. “Gplasdi: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder.” Computer Methods in Applied Mechanics and Engineering 418 (2024): 116535.</p></li>
-<li><p>He, Xiaolong, April Tran, David M. Bortz, and Youngsoo Choi. “Physics-informed active learning with simultaneous weak-form latent space dynamics identification.” arXiv preprint arXiv:2407.00337 (2024).</p></li>
-</ul>
+<p>&lt;&lt;&lt;&lt;&lt;&lt;&lt; HEAD
+* Bonneville, Christophe, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A. Messenger, Siu Wun Cheung et al. “A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling.” arXiv preprint arXiv:2403.10748 (2024).
+=======
+&gt;&gt;&gt;&gt;&gt;&gt;&gt; 9269e9cb2d85c993efecb52c726f2f1ff657d487
+* Fries, William D., Xiaolong He, and Youngsoo Choi. “LaSDI: Parametric latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 399 (2022): 115436.
+* He, Xiaolong, Youngsoo Choi, William D. Fries, Jonathan L. Belof, and Jiun-Shyan Chen. “gLaSDI: Parametric physics-informed greedy latent space dynamics identification.” Journal of Computational Physics 489 (2023): 112267.
+* Tran, April, Xiaolong He, Daniel A. Messenger, Youngsoo Choi, and David M. Bortz. “Weak-form latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 427 (2024): 116998.
+* Park, Jun Sur Richard, Siu Wun Cheung, Youngsoo Choi, and Yeonjong Shin. “tLaSDI: Thermodynamics-informed latent space dynamics identification.” arXiv preprint arXiv:2403.05848 (2024).
+* Bonneville, Christophe, Youngsoo Choi, Debojyoti Ghosh, and Jonathan L. Belof. “Gplasdi: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder.” Computer Methods in Applied Mechanics and Engineering 418 (2024): 116535.
+* He, Xiaolong, April Tran, David M. Bortz, and Youngsoo Choi. “Physics-informed active learning with simultaneous weak-form latent space dynamics identification.” arXiv preprint arXiv:2407.00337 (2024).</p>
 </section>
 
 
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diff --git a/searchindex.js b/searchindex.js
index 281c4e0..9c975b1 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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(class in lasdi.enums)": [[1, "lasdi.enums.Result"]], "runsample (lasdi.enums.nextstep attribute)": [[1, "lasdi.enums.NextStep.RunSample"]], "success (lasdi.enums.result attribute)": [[1, "lasdi.enums.Result.Success"]], "train (lasdi.enums.nextstep attribute)": [[1, "lasdi.enums.NextStep.Train"]], "unexecuted (lasdi.enums.result attribute)": [[1, "lasdi.enums.Result.Unexecuted"]], "lasdi.enums": [[1, "module-lasdi.enums"]], "module": [[1, "module-lasdi.enums"], [2, "module-lasdi.fd"], [3, "module-lasdi.gp"], [4, "module-lasdi.gplasdi"], [5, "module-lasdi"], [6, "module-lasdi.inputs"], [7, "module-lasdi.latent_dynamics"], [8, "module-lasdi.latent_dynamics.sindy"], [9, "module-lasdi.latent_space"], [10, "module-lasdi.param"], [11, "module-lasdi.physics.burgers1d"], [12, "module-lasdi.physics"], [13, "module-lasdi.postprocess"], [14, "module-lasdi.timing"], [15, "module-lasdi.workflow"]], "fddict (in module lasdi.fd)": [[2, "lasdi.fd.FDdict"]], "sbp12 (class in lasdi.fd)": [[2, "lasdi.fd.SBP12"]], "sbp24 (class in lasdi.fd)": [[2, "lasdi.fd.SBP24"]], "sbp36 (class in lasdi.fd)": [[2, "lasdi.fd.SBP36"]], "sbp48 (class in lasdi.fd)": [[2, "lasdi.fd.SBP48"]], "stencil (class in lasdi.fd)": [[2, "lasdi.fd.Stencil"]], "convert() (lasdi.fd.stencil method)": [[2, "lasdi.fd.Stencil.convert"]], "getoperators() (lasdi.fd.stencil method)": [[2, "lasdi.fd.Stencil.getOperators"]], "interiorindexes (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.interiorIndexes"]], "interiorindexes (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.interiorIndexes"]], "interiorindexes (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.interiorIndexes"]], "interiorindexes (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.interiorIndexes"]], "interiorindexes (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.interiorIndexes"]], "interiorstencils (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.interiorStencils"]], "interiorstencils (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.interiorStencils"]], "interiorstencils (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.interiorStencils"]], "interiorstencils (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.interiorStencils"]], "interiorstencils (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.interiorStencils"]], "lasdi.fd": [[2, "module-lasdi.fd"]], "leftbdrdepth (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.leftBdrDepth"]], "leftbdrdepth (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.leftBdrDepth"]], "leftbdrdepth (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.leftBdrDepth"]], "leftbdrdepth (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.leftBdrDepth"]], "leftbdrdepth (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrDepth"]], "leftbdrnorm (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrNorm"]], "leftbdrstencils (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrStencils"]], "leftbdrwidth (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrWidth"]], "eval_gp() (in module lasdi.gp)": [[3, "lasdi.gp.eval_gp"]], "fit_gps() (in module lasdi.gp)": [[3, "lasdi.gp.fit_gps"]], "lasdi.gp": [[3, "module-lasdi.gp"]], "sample_coefs() (in module lasdi.gp)": [[3, "lasdi.gp.sample_coefs"]], "bayesianglasdi (class in lasdi.gplasdi)": [[4, "lasdi.gplasdi.BayesianGLaSDI"]], "mse (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.MSE"]], "x_test (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.X_test"]], "x_train (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.X_train"]], "autoencoder (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.autoencoder"]], "average_rom() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.average_rom"]], "best_coefs (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.best_coefs"]], "best_loss (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.best_loss"]], "coef_weight (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.coef_weight"]], "export() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.export"]], "get_fom_max_std() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.get_fom_max_std"]], "get_new_sample_point() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.get_new_sample_point"]], "lasdi.gplasdi": [[4, "module-lasdi.gplasdi"]], "latent_dynamics (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.latent_dynamics"]], "ld_weight (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.ld_weight"]], "load() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.load"]], "lr (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.lr"]], "max_greedy_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.max_greedy_iter"]], "max_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.max_iter"]], "n_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.n_iter"]], "n_samples (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.n_samples"]], "optimizer (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.optimizer"]], "optimizer_to() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.optimizer_to"]], "param_space (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.param_space"]], "path_checkpoint (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.path_checkpoint"]], "path_results (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.path_results"]], "physics (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.physics"]], "restart_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.restart_iter"]], "sample_roms() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.sample_roms"]], "timer (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.timer"]], "train() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.train"]], "lasdi": [[5, "module-lasdi"]], "inputparser (class in lasdi.inputs)": [[6, "lasdi.inputs.InputParser"]], "dict_ (lasdi.inputs.inputparser attribute)": [[6, "lasdi.inputs.InputParser.dict_"]], "getinput() (lasdi.inputs.inputparser method)": [[6, "lasdi.inputs.InputParser.getInput"]], "lasdi.inputs": [[6, "module-lasdi.inputs"]], "name (lasdi.inputs.inputparser attribute)": [[6, "lasdi.inputs.InputParser.name"]], "verbose (in module lasdi.inputs)": [[6, "lasdi.inputs.verbose"]], "latentdynamics (class in lasdi.latent_dynamics)": [[7, "lasdi.latent_dynamics.LatentDynamics"]], "calibrate() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.calibrate"]], "coefs (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.coefs"]], "dim (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.dim"]], "export() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.export"]], "lasdi.latent_dynamics": [[7, "module-lasdi.latent_dynamics"]], "load() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.load"]], "ncoefs (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.ncoefs"]], "nt (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.nt"]], "sample() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.sample"]], "simulate() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.simulate"]], "mse (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.MSE"]], "sindy (class in lasdi.latent_dynamics.sindy)": [[8, "lasdi.latent_dynamics.sindy.SINDy"]], "calibrate() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.calibrate"]], "coef_norm_order (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.coef_norm_order"]], "compute_time_derivative() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.compute_time_derivative"]], "export() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.export"]], "fd (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd"]], "fd_oper (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd_oper"]], "fd_type (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd_type"]], "lasdi.latent_dynamics.sindy": [[8, "module-lasdi.latent_dynamics.sindy"]], "ncoefs (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.ncoefs"]], "simulate() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.simulate"]], "autoencoder (class in lasdi.latent_space)": [[9, "lasdi.latent_space.Autoencoder"]], "multilayerperceptron (class in lasdi.latent_space)": [[9, "lasdi.latent_space.MultiLayerPerceptron"]], "act_dict (in module lasdi.latent_space)": [[9, "lasdi.latent_space.act_dict"]], "act_type (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.act_type"]], "decoder (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.decoder"]], "encoder (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.encoder"]], "export() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.export"]], "forward() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.forward"]], "forward() (lasdi.latent_space.multilayerperceptron method)": [[9, "lasdi.latent_space.MultiLayerPerceptron.forward"]], "init_weight() (lasdi.latent_space.multilayerperceptron method)": [[9, "lasdi.latent_space.MultiLayerPerceptron.init_weight"]], "initial_condition_latent() (in module lasdi.latent_space)": [[9, "lasdi.latent_space.initial_condition_latent"]], "lasdi.latent_space": [[9, "module-lasdi.latent_space"]], "layer_sizes (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.layer_sizes"]], "layers (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.layers"]], "load() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.load"]], "n_layers (lasdi.latent_space.multilayerperceptron attribute)": [[9, 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method)": [[10, "lasdi.param.ParameterSpace.createHyperMeshGrid"]], "createinitialtrainspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createInitialTrainSpace"]], "createtestgridspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createTestGridSpace"]], "create_uniform_1dspace() (in module lasdi.param)": [[10, "lasdi.param.create_uniform_1dspace"]], "export() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.export"]], "getparam1dspace (in module lasdi.param)": [[10, "lasdi.param.getParam1DSpace"]], "getparameter() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.getParameter"]], "get_1dspace_from_list() (in module lasdi.param)": [[10, "lasdi.param.get_1dspace_from_list"]], "lasdi.param": [[10, "module-lasdi.param"]], "load() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.load"]], "n_init (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.n_init"]], "n_param (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.n_param"]], "n_test() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.n_test"]], "n_train() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.n_train"]], "param_list (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.param_list"]], "param_name (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.param_name"]], "test_grid_sizes (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_grid_sizes"]], "test_meshgrid (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_meshgrid"]], "test_space (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_space"]], "train_space (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.train_space"]], "burgers1d (class in lasdi.physics.burgers1d)": 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"lasdi.physics.burgers1d.jacobian"]], "lasdi.physics.burgers1d": [[11, "module-lasdi.physics.burgers1d"]], "main() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.main"]], "maxk (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.maxk"]], "nt (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.nt"]], "offline (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.offline"]], "qdim (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.qdim"]], "qgrid_size (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.qgrid_size"]], "residual() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.residual"]], "residual_burgers() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.residual_burgers"]], "solve() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.solve"]], "solver() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.solver"]], "t_grid (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.t_grid"]], "tmax (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.tmax"]], "w_idx (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.w_idx"]], "x_grid (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.x_grid"]], "xmax (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.xmax"]], "xmin (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.xmin"]], "offlinefom (class in lasdi.physics)": [[12, "lasdi.physics.OfflineFOM"]], "physics (class in lasdi.physics)": [[12, "lasdi.physics.Physics"]], "dim (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.dim"]], "dim (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.dim"]], "dt (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.dt"]], "dt (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.dt"]], "export() (lasdi.physics.offlinefom method)": [[12, "lasdi.physics.OfflineFOM.export"]], "export() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.export"]], "generate_solutions() (lasdi.physics.offlinefom method)": [[12, "lasdi.physics.OfflineFOM.generate_solutions"]], "generate_solutions() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.generate_solutions"]], "grid_size (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.grid_size"]], "grid_size (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.grid_size"]], "initial_condition() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.initial_condition"]], "lasdi.physics": [[12, "module-lasdi.physics"]], "nt (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.nt"]], "nt (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.nt"]], "offline (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.offline"]], "offline (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.offline"]], "param_name (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.param_name"]], "qdim (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.qdim"]], "qdim (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.qdim"]], "qgrid_size (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.qgrid_size"]], "qgrid_size (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.qgrid_size"]], "residual() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.residual"]], "solve() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.solve"]], "t_grid (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.t_grid"]], "t_grid (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.t_grid"]], "x_grid (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.x_grid"]], "x_grid (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.x_grid"]], "compute_errors() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.compute_errors"]], "heatmap2d() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.heatmap2d"]], "lasdi.postprocess": [[13, "module-lasdi.postprocess"]], "plot_gp2d() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.plot_gp2d"]], "plot_prediction() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.plot_prediction"]], "timer (class in lasdi.timing)": [[14, "lasdi.timing.Timer"]], "calls (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.calls"]], "end() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.end"]], "export() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.export"]], "lasdi.timing": [[14, "module-lasdi.timing"]], "load() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.load"]], "names (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.names"]], "print() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.print"]], "start() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.start"]], "starts (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.starts"]], "times (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.times"]], "collect_samples() (in module lasdi.workflow)": [[15, "lasdi.workflow.collect_samples"]], "initialize_latent_space() (in module lasdi.workflow)": [[15, "lasdi.workflow.initialize_latent_space"]], "initialize_physics() (in module lasdi.workflow)": [[15, "lasdi.workflow.initialize_physics"]], "initialize_trainer() (in module lasdi.workflow)": [[15, "lasdi.workflow.initialize_trainer"]], "lasdi.workflow": [[15, "module-lasdi.workflow"]], "latent_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.latent_dict"]], "ld_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.ld_dict"]], "main() (in module lasdi.workflow)": [[15, "lasdi.workflow.main"]], "parser (in module lasdi.workflow)": [[15, "lasdi.workflow.parser"]], "physics_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.physics_dict"]], "pick_samples() (in module lasdi.workflow)": [[15, "lasdi.workflow.pick_samples"]], "run_samples() (in module lasdi.workflow)": [[15, "lasdi.workflow.run_samples"]], "step() (in module lasdi.workflow)": [[15, "lasdi.workflow.step"]], "trainer_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.trainer_dict"]]}})
\ No newline at end of file