Merge branch 'research' of https://github.com/AstitvaAggarwal/NeuralP…

…DE.jl into research

Project.toml

@@ -115,27 +115,19 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DiffEqNoiseProcess = "77a26b50-5914-5dd7-bc55-306e6241c503"
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 DiffEqNoiseProcess = "77a26b50-5914-5dd7-bc55-306e6241c503"
-ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Hwloc = "0e44f5e4-bd66-52a0-8798-143a42290a1d"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
-Hwloc = "0e44f5e4-bd66-52a0-8798-143a42290a1d"
-InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
 LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
 LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623"
 LuxLib = "82251201-b29d-42c6-8e01-566dec8acb11"
-LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623"
-LuxLib = "82251201-b29d-42c6-8e01-566dec8acb11"
 MethodOfLines = "94925ecb-adb7-4558-8ed8-f975c56a0bf4"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 TensorBoardLogger = "899adc3e-224a-11e9-021f-63837185c80f"
-ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
-StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
-TensorBoardLogger = "899adc3e-224a-11e9-021f-63837185c80f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]

src/training_strategies.jl

@@ -46,6 +46,38 @@ function get_dataset_train_points(eqs, train_sets, pinnrep)
     return points
 end
 
+# dataset must have depvar values for same values of indvars
+function get_dataset_train_points(eqs, train_sets, pinnrep)
+    dict_depvar_input = pinnrep.dict_depvar_input
+    depvars = pinnrep.depvars
+    dict_depvars = pinnrep.dict_depvars
+    dict_indvars = pinnrep.dict_indvars
+
+    symbols_input = [(i, dict_depvar_input[i]) for i in depvars]
+    # [(:u, [:t])]
+    eq_args = NeuralPDE.get_argument(eqs, dict_indvars, dict_depvars)
+    # equation wise indvar presence ~ [[:t]]
+    # in each equation atleast one depvars must be a function of all indvars(to cover heterogenous/not case)
+
+    # train_sets follows order of depvars
+    # take dataset indvar values if for equations depvar's indvar matches input symbol indvar
+    points = []
+    for eq_arg in eq_args
+        eq_points = []
+        for i in eachindex(symbols_input)
+            if symbols_input[i][2] == eq_arg
+                push!(eq_points, train_sets[i][:, 2:end]')
+                # Terminate to avoid repetitive ind var points inclusion
+                break
+            end
+        end
+        # Concatenate points for this equation argument
+        push!(points, vcat(eq_points...))
+    end
+
+    return points
+end
+
 # include dataset points in pde_residual loglikelihood (BayesianPINN)
 function merge_strategy_with_loglikelihood_function(pinnrep::PINNRepresentation,
         strategy::GridTraining, datafree_pde_loss_function,
@@ -105,6 +137,18 @@ function get_points_loss_functions(loss_function, train_set, eltypeθ, strategy:
     end
 end
 
+function get_points_loss_functions(loss_function, train_set, eltypeθ, strategy::GridTraining;
+        τ = nothing)
+        # loss_function length is number of all points loss is being evaluated upon
+        # train sets rows are for each indvar, cols are coordinates (row_1,row_2,..row_n) at which loss evaluated
+    function loss(θ, std)
+        logpdf(
+            MvNormal(loss_function(train_set, θ)[1, :],
+                LinearAlgebra.Diagonal(abs2.(std .* ones(size(train_set)[2])))),
+            zeros(size(train_set)[2]))
+    end
+end
+
 function merge_strategy_with_loss_function(pinnrep::PINNRepresentation,
         strategy::GridTraining, datafree_pde_loss_function, datafree_bc_loss_function)
     (; domains, eqs, bcs, dict_indvars, dict_depvars) = pinnrep
@@ -127,6 +171,8 @@ function merge_strategy_with_loss_function(pinnrep::PINNRepresentation,
     bc_loss_functions = [get_loss_function(_loss, _set, eltypeθ, strategy)
                          for (_loss, _set) in zip(datafree_bc_loss_function,
                                                    bcs_train_sets)]
+                         for (_loss, _set) in zip(datafree_bc_loss_function,
+                                                   bcs_train_sets)]
     pde_loss_functions, bc_loss_functions
 end