test API

test/PINO_PDE_tests.jl

@@ -1,10 +1,61 @@
 using Test
-using OrdinaryDiffEq, OptimizationOptimisers
+using Optimization, OptimizationOptimJL, OptimizationOptimisers
 using Lux
 using Statistics, Random
+using ModelingToolkit
+import ModelingToolkit: Interval, infimum, supremum
+using DomainSets
 using NeuralPDE
 
 
-@testset "Example du = cos(p * t)" begin
-    #TODO
-end
+##example ODE
+@parameters t
+@variables u(..)
+# @parameters p #[bounds = (0.1f0, pi)]
+Dt = Differential(t)
+eq = [Dt(u(t)) ~ cos(t)]
+bc = [u(0) ~ 1.0f0]
+
+dom = [x ∈ Interval(0.0, 1.0)]
+# neural_operator = SomeNeuralOperator(some_args)
+neural_operator = Lux.Chain(
+    Lux.Dense(1, 10, Lux.tanh),
+    Lux.Dense(10, 10, Lux.tanh),
+    Lux.Dense(10, 1))
+
+# pino = PhysicsInformedNO(neural_operator, sometrainig)
+pino = NeuralPDE.PhysicsInformedNN(neural_operator, NeuralPDE.GridTraining(0.1))
+
+@named pde_system = PDESystem(eq, bc, dom, [t], [u(t)])  #[p]; defaults = Dict([p => 1.0 for p in [p]]))
+
+# hasbounds(pde_system.ps[1])
+# getbounds(pde_system.ps[1])
+
+prob = discretize(pde_system, pino)
+sym_prob = symbolic_discretize(pde_system, pino)
+
+res = Optimization.solve(prob, ADAM(0.1); maxiters = 4000)
+phi = discretization.phi
+
+
+@parameters x
+@parameters p [bounds = (0.1f0, pi)]
+@variables u(..)
+Dx = Differential(x)
+
+eq = Dx(u(x)) ~ cos(p * x)
+bcs = [u(0.0) ~ 0.0]
+
+domains = [x ∈ Interval(0.0, 1.0)]
+
+chain = [Lux.Chain(Lux.Dense(1, 12, Lux.tanh), Lux.Dense(12, 12, Lux.tanh), Lux.Dense(12, 1)) ]
+
+strategy = NeuralPDE.GridTraining(0.1)
+discretization = NeuralPDE.PhysicsInformedNN(chain, strategy)
+
+@named pde_system = PDESystem(eq, bcs, domains, [x], [u(x)],[p]; defaults = Dict([p => 1.0 for p in [p]]))
+hasbounds(pde_system.ps[1])
+getbounds(pde_system.ps[1])
+
+prob = discretize(pde_system, discretization)
+sym_prob = symbolic_discretize(pde_system, discretization)