changes from reviews

src/BPINN_ode.jl

@@ -148,7 +148,7 @@ end
 BPINN Solution contains the original solution from AdvancedHMC.jl sampling(BPINNstats contains fields related to that)
 > ensemblesol is the Probabilistic Estimate(MonteCarloMeasurements.jl Particles type) of Ensemble solution from All Neural Network's(made using all sampled parameters) output's.
 > estimated_nn_params - Probabilistic Estimate of NN params from sampled weights,biases
-> estimated_de_params - Probabilistic Estimate of DE params from sampled unknown de paramters
+> estimated_de_params - Probabilistic Estimate of DE params from sampled unknown DE paramters
 """
 
 struct BPINNsolution{O <: BPINNstats, E, NP, OP, P}

src/PDE_BPINN.jl

@@ -417,11 +417,11 @@ function ahmc_bayesian_pinn_pde(pde_system, discretization;
     metric = Metric(nparameters)
     hamiltonian = Hamiltonian(metric, ℓπ, ForwardDiff)
 
-    println("Current Physics Log-likelihood : ",
+    @info("Current Physics Log-likelihood : ",
         ℓπ.full_loglikelihood(setparameters(ℓπ, initial_θ),
             ℓπ.allstd))
-    println("Current Prior Log-likelihood : ", priorlogpdf(ℓπ, initial_θ))
-    println("Current MSE against dataset Log-likelihood : ", L2LossData(ℓπ, initial_θ))
+    @info("Current Prior Log-likelihood : ", priorlogpdf(ℓπ, initial_θ))
+    @info("Current MSE against dataset Log-likelihood : ", L2LossData(ℓπ, initial_θ))
 
     # parallel sampling option
     if nchains != 1
@@ -476,12 +476,12 @@ function ahmc_bayesian_pinn_pde(pde_system, discretization;
         matrix_samples = hcat(samples...)
         mcmc_chain = MCMCChains.Chains(matrix_samples')
 
-        println("Sampling Complete.")
-        println("Current Physics Log-likelihood : ",
+        @info("Sampling Complete.")
+        @info("Current Physics Log-likelihood : ",
             ℓπ.full_loglikelihood(setparameters(ℓπ, samples[end]),
                 ℓπ.allstd))
-        println("Current Prior Log-likelihood : ", priorlogpdf(ℓπ, samples[end]))
-        println("Current MSE against dataset Log-likelihood : ",
+        @info("Current Prior Log-likelihood : ", priorlogpdf(ℓπ, samples[end]))
+        @info("Current MSE against dataset Log-likelihood : ",
             L2LossData(ℓπ, samples[end]))
 
         fullsolution = BPINNstats(mcmc_chain, samples, stats)

src/advancedHMC_MCMC.jl

@@ -65,7 +65,7 @@ mutable struct LogTargetDensity{C, S, ST <: AbstractTrainingStrategy, I,
 end
 
 """
-Cool function needed for converting vector of sampled parameters into ComponentVector in case of Lux chain output, derivatives
+function needed for converting vector of sampled parameters into ComponentVector in case of Lux chain output, derivatives
 the sampled parameters are of exotic type `Dual` due to ForwardDiff's autodiff tagging
 """
 function vector_to_parameters(ps_new::AbstractVector,
@@ -558,9 +558,9 @@ function ahmc_bayesian_pinn_ode(prob::DiffEqBase.ODEProblem, chain;
         end
     end
 
-    println("Current Physics Log-likelihood : ", physloglikelihood(ℓπ, initial_θ))
-    println("Current Prior Log-likelihood : ", priorweights(ℓπ, initial_θ))
-    println("Current MSE against dataset Log-likelihood : ", L2LossData(ℓπ, initial_θ))
+    @info("Current Physics Log-likelihood : ", physloglikelihood(ℓπ, initial_θ))
+    @info("Current Prior Log-likelihood : ", priorweights(ℓπ, initial_θ))
+    @info("Current MSE against dataset Log-likelihood : ", L2LossData(ℓπ, initial_θ))
 
     Adaptor, Metric, targetacceptancerate = Adaptorkwargs[:Adaptor],
     Adaptorkwargs[:Metric], Adaptorkwargs[:targetacceptancerate]
@@ -608,10 +608,10 @@ function ahmc_bayesian_pinn_ode(prob::DiffEqBase.ODEProblem, chain;
         samples, stats = sample(hamiltonian, Kernel, initial_θ, draw_samples,
             adaptor; progress = progress, verbose = verbose)
 
-        println("Sampling Complete.")
-        println("Current Physics Log-likelihood : ", physloglikelihood(ℓπ, samples[end]))
-        println("Current Prior Log-likelihood : ", priorweights(ℓπ, samples[end]))
-        println("Current MSE against dataset Log-likelihood : ",
+        @info("Sampling Complete.")
+        @info("Current Physics Log-likelihood : ", physloglikelihood(ℓπ, samples[end]))
+        @info("Current Prior Log-likelihood : ", priorweights(ℓπ, samples[end]))
+        @info("Current MSE against dataset Log-likelihood : ",
             L2LossData(ℓπ, samples[end]))
 
         # return a chain(basic chain),samples and stats

src/discretize.jl

@@ -401,7 +401,7 @@ to the PDE.
 For more information, see `discretize` and `PINNRepresentation`.
 """
 function SciMLBase.symbolic_discretize(pde_system::PDESystem,
-    discretization::PhysicsInformedNN; bayesian::Bool = false,dataset_given=[nothing])
+    discretization::PhysicsInformedNN; bayesian::Bool = false,dataset_given=nothing)
     eqs = pde_system.eqs
     bcs = pde_system.bcs
     chain = discretization.chain
@@ -587,7 +587,7 @@ function SciMLBase.symbolic_discretize(pde_system::PDESystem,
 
     if bayesian
         # required as Physics loss also needed on dataset domain points
-        pde_loss_functions1, bc_loss_functions1 = if !(dataset_given isa Vector{Nothing})
+        pde_loss_functions1, bc_loss_functions1 = if !(dataset_given isa Nothing)
             if !(strategy isa  GridTraining)
                 throw("only GridTraining strategy allowed")
             else
@@ -609,7 +609,7 @@ function SciMLBase.symbolic_discretize(pde_system::PDESystem,
             bc_loglikelihoods = [logpdf(Normal(0, stdbcs[j]), bc_loss_function(θ))
                                  for (j, bc_loss_function) in enumerate(bc_loss_functions)]    
 
-            if !(dataset_given isa Vector{Nothing})
+            if !(dataset_given isa Nothing)
                 pde_loglikelihoods += [logpdf(Normal(0, stdpdes[j]), pde_loss_function1(θ))
                                        for (j, pde_loss_function1) in enumerate(pde_loss_functions1)]
 

test/BPINN_PDEinvsol_tests.jl

@@ -149,8 +149,7 @@ sol1 = ahmc_bayesian_pinn_pde(pde_system,
     priorsNNw = (0.0, 1.0),
     saveats = [0.01],
     param = [Normal(12.0, 2)],
-    dataset = dataset,
-    progress = true)
+    dataset = dataset)
 
 idealp = 10.0
 p_ = sol1.estimated_de_params