Merge branch 'SciML:master' into develop

Project.toml

@@ -63,7 +63,7 @@ Lux = "0.4, 0.5"
 MCMCChains = "6"
 ModelingToolkit = "8"
 Optim = "1.0"
-Optimisers = "0.2"
+Optimisers = "0.2, 0.3"
 Optimization = "3"
 QuasiMonteCarlo = "0.2.1"
 RecursiveArrayTools = "2.31"

src/ode_solve.jl

@@ -308,13 +308,13 @@ function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Boo
     weights = strategy.weights ./ sum(strategy.weights)
 
     N = length(weights)
-    samples = strategy.samples
+    points = strategy.points
 
     difference = (maxT - minT) / N
 
     data = Float64[]
     for (index, item) in enumerate(weights)
-        temp_data = rand(1, trunc(Int, samples * item)) .* difference .+ minT .+
+        temp_data = rand(1, trunc(Int, points * item)) .* difference .+ minT .+
                     ((index - 1) * difference)
         data = append!(data, temp_data)
     end
@@ -450,19 +450,17 @@ function DiffEqBase.__solve(prob::DiffEqBase.AbstractODEProblem,
         if !(tstops isa Nothing)
             num_tstops_points = length(tstops)
             tstops_loss_func = evaluate_tstops_loss(phi, f, autodiff, tstops, p, batch) 
-            total_tstops_loss = tstops_loss_func(θ, phi) * num_tstops_points
+            tstops_loss = tstops_loss_func(θ, phi)
             if strategy isa GridTraining 
                 num_original_points = length(tspan[1]:(strategy.dx):tspan[2])
-            elseif strategy isa WeightedIntervalTraining
-                num_original_points = strategy.samples
-            elseif strategy isa StochasticTraining
+            elseif strategy isa Union{WeightedIntervalTraining, StochasticTraining}
                 num_original_points = strategy.points
             else
-                L2_loss = L2_loss + tstops_loss_func(θ, phi)
-                return L2_loss
+                return L2_loss + tstops_loss
             end
 
             total_original_loss = L2_loss * num_original_points
+            total_tstops_loss = tstops_loss * num_original_points
             total_points = num_original_points + num_tstops_points
             L2_loss = (total_original_loss + total_tstops_loss) / total_points
 

src/training_strategies.jl

@@ -312,19 +312,19 @@ such that the total number of sampled points is equivalent to the given samples
 ## Positional Arguments
 
 * `weights`: A vector of weights that should sum to 1, representing the proportion of samples at each interval.
-* `samples`: the total number of samples that we want, across the entire time span
+* `points`: the total number of samples that we want, across the entire time span
 
 ## Limitations
 
 This training strategy can only be used with ODEs (`NNODE`).
 """
 struct WeightedIntervalTraining{T} <: AbstractTrainingStrategy
     weights::Vector{T}
-    samples::Int
+    points::Int
 end
 
-function WeightedIntervalTraining(weights, samples)
-    WeightedIntervalTraining(weights, samples)
+function WeightedIntervalTraining(weights, points)
+    WeightedIntervalTraining(weights, points)
 end
 
 function get_loss_function(loss_function, train_set, eltypeθ,

test/NNODE_tests.jl

@@ -239,9 +239,9 @@ chain = Lux.Chain(Lux.Dense(1, N, func), Lux.Dense(N, N, func), Lux.Dense(N, N,
 
 opt = Optimisers.Adam(0.01)
 weights = [0.7, 0.2, 0.1]
-samples = 200
+points = 200
 alg = NeuralPDE.NNODE(chain, opt, autodiff = false,
-                      strategy = NeuralPDE.WeightedIntervalTraining(weights, samples))
+                      strategy = NeuralPDE.WeightedIntervalTraining(weights, points))
 sol = solve(prob_oop, alg, verbose = true, maxiters = 100000, saveat = 0.01)
 
 @test abs(mean(sol) - mean(true_sol)) < 0.2

test/NNODE_tstops_test.jl

@@ -27,7 +27,7 @@ threshold = 0.2
 
 #bad choices for weights, samples and dx so that the algorithm will fail without the added points
 weights = [0.3, 0.3, 0.4]
-samples = 3
+points = 3
 dx = 1.0
 
 #Grid Training without added points (difference between solutions should be high)
@@ -43,25 +43,25 @@ sol = solve(prob_oop, alg, verbose=true, maxiters = maxiters, saveat = saveat, t
 @test abs(mean(sol) - mean(true_sol)) < threshold
 
 #WeightedIntervalTraining without added points (difference between solutions should be high)
-alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.WeightedIntervalTraining(weights, samples))
+alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.WeightedIntervalTraining(weights, points))
 sol = solve(prob_oop, alg, verbose=true, maxiters = maxiters, saveat = saveat)
 
 @test abs(mean(sol) - mean(true_sol)) > threshold
 
 #WeightedIntervalTraining with added points (difference between solutions should be low)
-alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.WeightedIntervalTraining(weights, samples))
+alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.WeightedIntervalTraining(weights, points))
 sol = solve(prob_oop, alg, verbose=true, maxiters = maxiters, saveat = saveat, tstops = addedPoints)
 
 @test abs(mean(sol) - mean(true_sol)) < threshold
 
 #StochasticTraining without added points (difference between solutions should be high)
-alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.StochasticTraining(samples))
+alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.StochasticTraining(points))
 sol = solve(prob_oop, alg, verbose=true, maxiters = maxiters, saveat = saveat)
 
 @test abs(mean(sol) - mean(true_sol)) > threshold
 
 #StochasticTraining with added points (difference between solutions should be low)
-alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.StochasticTraining(samples))
+alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.StochasticTraining(points))
 sol = solve(prob_oop, alg, verbose=true, maxiters = maxiters, saveat = saveat, tstops = addedPoints)
 
 @test abs(mean(sol) - mean(true_sol)) < threshold