Initial commit for GivenPointsTraining

src/NeuralPDE.jl

@@ -56,7 +56,7 @@ export NNODE, NNDAE, TerminalPDEProblem, NNPDEHan, NNPDENS, NNRODE,
  KolmogorovParamDomain, NNParamKolmogorov,
  PhysicsInformedNN, discretize,
  GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining,
- WeightedIntervalTraining,
+ WeightedIntervalTraining, GivenPointsTraining,
  build_loss_function, get_loss_function,
  generate_training_sets, get_variables, get_argument, get_bounds,
  get_phi, get_numeric_derivative, get_numeric_integral,

src/ode_solve.jl

@@ -382,6 +382,20 @@ function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Boo
  return loss
 end
 
+function generate_loss(strategy::GivenPointsTraining, phi, f, autodiff::Bool, tspan, p, batch)
+ ts =strategy.given_points
+
+ # sum(abs2,inner_loss(t,θ) for t in ts) but Zygote generators are broken
+ function loss(θ, _)
+ if batch
+ sum(abs2, inner_loss(phi, f, autodiff, ts, θ, p))
+ else
+ sum(abs2, [inner_loss(phi, f, autodiff, t, θ, p) for t in ts])
+ end
+ end
+ return loss
+end
+
 function generate_loss(strategy::QuasiRandomTraining, phi, f, autodiff::Bool, tspan)
  error("QuasiRandomTraining is not supported by NNODE since it's for high dimensional spaces only. Use StochasticTraining instead.")
 end

src/training_strategies.jl

@@ -332,3 +332,17 @@ function get_loss_function(loss_function, train_set, eltypeθ,
  τ = nothing)
  loss = (θ) -> mean(abs2, loss_function(train_set, θ))
 end
+
+struct GivenPointsTraining{T} <: AbstractTrainingStrategy
+ given_points::Vector{T}
+end
+
+function GivenPointsTraining(given_points)
+ GivenPointsTraining(given_points)
+end
+
+function get_loss_function(loss_function, train_set, eltypeθ,
+ strategy::GivenPointsTraining;
+ τ = nothing)
+ loss = (θ) -> mean(abs2, loss_function(train_set, θ))
+end

test/given_points_training_test.jl

@@ -0,0 +1,33 @@
+
+using OrdinaryDiffEq, OptimizationPolyalgorithms, Lux, OptimizationOptimJL, Test, Statistics, Plots, Optimisers
+
+function fu(u, p, t)
+ [p[1] * u[1] - p[2] * u[1] * u[2], -p[3] * u[2] + p[4] * u[1] * u[2]]
+end
+
+p = [1.5, 1.0, 3.0, 1.0]
+u0 = [1.0, 1.0]
+tspan = (0.0, 3.0)
+points1 = [rand() for i=1:140]
+points2 = [rand() + 1 for i=1:40]
+points3 = [rand() + 2 for i=1:20]
+points = vcat(points1, points2, points3)
+
+prob_oop = ODEProblem{false}(fu, u0, tspan, p)
+true_sol = solve(prob_oop, Tsit5(), saveat = 0.01)
+func = Lux.σ
+N = 12
+chain = Lux.Chain(Lux.Dense(1, N, func), Lux.Dense(N, N, func), Lux.Dense(N, N, func),
+ Lux.Dense(N, N, func), Lux.Dense(N, length(u0)))
+
+opt = Optimisers.Adam(0.01)
+alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.GivenPointsTraining(points))
+sol = solve(prob_oop, alg, verbose=true, maxiters = 100000, saveat = 0.01)
+
+@test abs(mean(sol) - mean(true_sol)) < 0.2
+
+using Plots
+
+plot(sol)
+plot!(true_sol)
+ylims!(0,8)