BPINN PDE solver

.github/workflows/CI.yml

@@ -18,6 +18,7 @@ jobs:
       matrix:
         group:
           - ODEBPINN
+          - PDEBPINN
           - NNPDE1
           - NNPDE2
           - AdaptiveLoss

docs/src/tutorials/Lotka_Volterra_BPINNs.md

@@ -108,7 +108,7 @@ plot!(solution, labels = ["true x" "true y"])
 We can see the estimated ODE parameters by -
 
 ```@example bpinn
-sol_pestim.estimated_ode_params
+sol_pestim.estimated_de_params
 ```
 
 We can see it is close to the true values of the parameters.

src/BPINN_ode.jl

@@ -148,21 +148,24 @@ 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_ode_params - Probabilistic Estimate of ODE params from sampled unknown ode paramters
+> estimated_de_params - Probabilistic Estimate of DE params from sampled unknown de paramters
 """
-struct BPINNsolution{O <: BPINNstats, E,
-    NP <: Vector{<:MonteCarloMeasurements.Particles{<:Float64}},
-    OP <: Union{Vector{Nothing},
-        Vector{<:MonteCarloMeasurements.Particles{<:Float64}}}}
+
+struct BPINNsolution{O <: BPINNstats, E, NP, OP, P}
     original::O
     ensemblesol::E
     estimated_nn_params::NP
-    estimated_ode_params::OP
-
-    function BPINNsolution(original, ensemblesol, estimated_nn_params, estimated_ode_params)
+    estimated_de_params::OP
+    timepoints::P
+
+    function BPINNsolution(original,
+            ensemblesol,
+            estimated_nn_params,
+            estimated_de_params,
+            timepoints)
         new{typeof(original), typeof(ensemblesol), typeof(estimated_nn_params),
-            typeof(estimated_ode_params)}(original, ensemblesol, estimated_nn_params,
-            estimated_ode_params)
+            typeof(estimated_de_params), typeof(timepoints)}(original, ensemblesol, estimated_nn_params,
+            estimated_de_params, timepoints)
     end
 end
 
@@ -260,14 +263,14 @@ function DiffEqBase.__solve(prob::DiffEqBase.ODEProblem,
     end
 
     nnparams = length(θinit)
-    estimnnparams = [Particles(reduce(hcat, samples)[i, :]) for i in 1:nnparams]
+    estimnnparams = [Particles(reduce(hcat, samples[(end - numensemble):end])[i, :]) for i in 1:nnparams]
 
     if ninv == 0
         estimated_params = [nothing]
     else
-        estimated_params = [Particles(reduce(hcat, samples[(end - ninv + 1):end])[i, :])
+        estimated_params = [Particles(reduce(hcat, samples[(end - numensemble):end])[i, :])
                             for i in (nnparams + 1):(nnparams + ninv)]
     end
 
-    BPINNsolution(fullsolution, ensemblecurves, estimnnparams, estimated_params)
+    BPINNsolution(fullsolution, ensemblecurves, estimnnparams, estimated_params, t)
 end

src/NeuralPDE.jl

@@ -52,19 +52,21 @@ include("discretize.jl")
 include("neural_adapter.jl")
 include("advancedHMC_MCMC.jl")
 include("BPINN_ode.jl")
+include("PDE_BPINN.jl")
 
 export NNODE, TerminalPDEProblem, NNPDEHan, NNPDENS, NNRODE,
-       KolmogorovPDEProblem, NNKolmogorov, NNStopping, ParamKolmogorovPDEProblem,
-       KolmogorovParamDomain, NNParamKolmogorov,
-       PhysicsInformedNN, discretize,
-       GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining,
-       WeightedIntervalTraining,
-       build_loss_function, get_loss_function,
-       generate_training_sets, get_variables, get_argument, get_bounds,
-       get_phi, get_numeric_derivative, get_numeric_integral,
-       build_symbolic_equation, build_symbolic_loss_function, symbolic_discretize,
-       AbstractAdaptiveLoss, NonAdaptiveLoss, GradientScaleAdaptiveLoss,
-       MiniMaxAdaptiveLoss,
-       LogOptions, ahmc_bayesian_pinn_ode, BNNODE
+    KolmogorovPDEProblem, NNKolmogorov, NNStopping, ParamKolmogorovPDEProblem,
+    KolmogorovParamDomain, NNParamKolmogorov,
+    PhysicsInformedNN, discretize,
+    GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining,
+    WeightedIntervalTraining,
+    build_loss_function, get_loss_function,
+    generate_training_sets, get_variables, get_argument, get_bounds,
+    get_phi, get_numeric_derivative, get_numeric_integral,
+    build_symbolic_equation, build_symbolic_loss_function, symbolic_discretize,
+    AbstractAdaptiveLoss, NonAdaptiveLoss, GradientScaleAdaptiveLoss,
+    MiniMaxAdaptiveLoss, LogOptions,
+    ahmc_bayesian_pinn_ode, BNNODE, ahmc_bayesian_pinn_pde, vector_to_parameters,
+    BPINNsolution
 
 end # module