Merge branch 'SciML:master' into adaptive-activation-functions

Project.toml

@@ -1,7 +1,7 @@
 name = "NeuralPDE"
 uuid = "315f7962-48a3-4962-8226-d0f33b1235f0"
 authors = ["Chris Rackauckas <[email protected]>"]
-version = "4.5.1"
+version = "4.6.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

docs/src/solvers/pinns.md

@@ -54,10 +54,10 @@ List of training strategies that are available now:
    `dx` is a scalar, then `dx` corresponds to the spacing in each direction. If `dx`
    is a vector, then it should be sized to match the number of dimensions and corresponds
    to the spacing per direction.
- - `StochasticTraining(points:bcs_points = ponits)`: `points` is number of stochastically sampled points from the domain,
+ - `StochasticTraining(points;bcs_points = points)`: `points` is number of stochastically sampled points from the domain,
     `bcs_points` is number of points for boundary conditions(by default, it equals `points`).   
    In each optimization iteration, we randomly select a new subset of points from a full training set.
- - `QuasiRandomTraining(points;bcs_points = ponits, sampling_alg = UniformSample(), resampling = true, minibatch=500)`:
+ - `QuasiRandomTraining(points;bcs_points = points, sampling_alg = UniformSample(), resampling = true, minibatch=500)`:
    The training set is generated on quasi-random low discrepency sequences.
    `points` is the number of quasi-random points in every subset or set, `bcs_points` is number of points for boundary conditions(by default, it equals `points`), `sampling_alg` is the quasi-Monte Carlo sampling algorithm. `if resampling = false`, the full training set is generated in advance before training, and at each iteration, one subset is randomly selected out of the batch.`minibatch` is the number of subsets in full training set.
    The number of the total points is `length(lb) * points * minibatch`, where `lb` is the lower bound and `length(lb)` is the dimensionality.

lib/NeuralPDELogging/LICENSE

@@ -0,0 +1,9 @@
+The NeuralPDE.jl package is licensed under the MIT "Expat" License:
+
+Copyright (c) 2017: ChrisRackauckas.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

lib/NeuralPDELogging/Project.toml

@@ -8,6 +8,11 @@ Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 NeuralPDE = "315f7962-48a3-4962-8226-d0f33b1235f0"
 TensorBoardLogger = "899adc3e-224a-11e9-021f-63837185c80f"
 
+[compat]
+NeuralPDE = "4"
+TensorBoardLogger = "0.1"
+julia = "1.6"
+
 [extras]
 DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 GalacticOptim = "a75be94c-b780-496d-a8a9-0878b188d577"