refactor: use latest API for IntegralProblem

.github/workflows/CI.yml

@@ -25,7 +25,6 @@ jobs:
           - AdaptiveLoss
           - Logging
           - Forward
-          - NeuralAdapter
         version:
           - "1"
     steps:

lib/NeuralPDELogging/test/adaptive_loss_log_tests.jl

@@ -88,7 +88,7 @@ function test_2d_poisson_equation_adaptive_loss(adaptive_loss, run, outdir, hasl
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.03); maxiters = maxiters,
                              callback = callback)
 
-    u_predict = reshape([first(phi([x, y], res.minimizer)) for x in xs for y in ys],
+    u_predict = reshape([first(phi([x, y], res.u)) for x in xs for y in ys],
                         (length(xs), length(ys)))
     diff_u = abs.(u_predict .- u_real)
     total_diff = sum(diff_u)

src/advancedHMC_MCMC.jl

@@ -176,7 +176,7 @@ function getlogpdf(strategy::QuadratureTraining, Tar::LogTargetDensity, f,
     function integrand(t::Number, θ)
         innerdiff(Tar, f, autodiff, [t], θ, ode_params)
     end
-    intprob = IntegralProblem(integrand, tspan[1], tspan[2], θ; nout = length(Tar.prob.u0))
+    intprob = IntegralProblem(integrand, (tspan[1], tspan[2]), θ; nout = length(Tar.prob.u0))
     sol = solve(intprob, QuadGKJL(); abstol = strategy.abstol, reltol = strategy.reltol)
     sum(sol.u)
 end

src/discretize.jl

@@ -317,7 +317,7 @@ function get_numeric_integral(pinnrep::PINNRepresentation)
                 ChainRulesCore.@ignore_derivatives @views(cord_[integrating_var_id]) .= x
                 return integrand_func(cord_, p, phi, derivative, nothing, u, nothing)
             end
-            prob_ = IntegralProblem(integrand_, lb, ub, θ)
+            prob_ = IntegralProblem(integrand_, (lb, ub), θ)
             sol = solve(prob_, CubatureJLh(), reltol = 1e-3, abstol = 1e-3)[1]
 
             return sol

src/ode_solve.jl

@@ -232,7 +232,7 @@ function generate_loss(strategy::QuadratureTraining, phi, f, autodiff::Bool, tsp
     @assert batch == 0 # not implemented
 
     function loss(θ, _)
-        intprob = IntegralProblem(integrand, tspan[1], tspan[2], θ)
+        intprob = IntegralProblem(integrand, (tspan[1], tspan[2]), θ)
         sol = solve(intprob, QuadGKJL(); abstol = strategy.abstol, reltol = strategy.reltol)
         sol.u
     end

src/training_strategies.jl

@@ -315,7 +315,7 @@ function get_loss_function(loss_function, lb, ub, eltypeθ, strategy::Quadrature
             sum(abs2, view(loss_(x, θ), 1, :), dims = 2) #./ size_x
         end
         integral_function = BatchIntegralFunction(integrand, max_batch = strategy.batch)
-        prob = IntegralProblem(integral_function, lb, ub, θ)
+        prob = IntegralProblem(integral_function, (lb, ub), θ)
         solve(prob,
               strategy.quadrature_alg,
               reltol = strategy.reltol,

test/IDE_tests.jl

@@ -30,7 +30,7 @@ end
     phi = discretization.phi
     analytic_sol_func(t) = 1 / 2 * (exp(-t)) * (sin(2 * t))
     u_real = [analytic_sol_func(t) for t in ts]
-    u_predict = [first(phi([t], res.minimizer)) for t in ts]
+    u_predict = [first(phi([t], res.u)) for t in ts]
     @test Flux.mse(u_real, u_predict) < 0.01
 end
 
@@ -54,7 +54,7 @@ eq = Ix(u(x) * cos(x)) ~ (x^3) / 3
                             maxiters = 200)
     xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
     phi = discretization.phi
-    u_predict = [first(phi([x], res.minimizer)) for x in xs]
+    u_predict = [first(phi([x], res.u)) for x in xs]
     u_real = [x^2 / cos(x) for x in xs]
     @test Flux.mse(u_real, u_predict) < 0.001
 end
@@ -78,7 +78,7 @@ end
     ys = 0.00:0.01:1.00
     phi = discretization.phi
     u_real = collect(1 - x^2 - y^2 for y in ys, x in xs);
-    u_predict = collect(Array(phi([x, y], res.minimizer))[1] for y in ys, x in xs);
+    u_predict = collect(Array(phi([x, y], res.u))[1] for y in ys, x in xs);
     @test Flux.mse(u_real, u_predict) < 0.001
 end
 
@@ -101,7 +101,7 @@ end
     ys = 0.00:0.01:1.00
     phi = discretization.phi
     u_real = collect(x + y^2 for y in ys, x in xs);
-    u_predict = collect(Array(phi([x, y], res.minimizer))[1] for y in ys, x in xs);
+    u_predict = collect(Array(phi([x, y], res.u))[1] for y in ys, x in xs);
     @test Flux.mse(u_real, u_predict) < 0.01
 end
 
@@ -144,7 +144,7 @@ end
     res = solve(prob, OptimizationOptimJL.BFGS(); callback = callback, maxiters = 200)
     xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
     phi = discretization.phi
-    u_predict = [first(phi([x], res.minimizer)) for x in xs]
+    u_predict = [first(phi([x], res.u)) for x in xs]
     u_real = [1 / x^2 for x in xs]
     @test u_real≈u_predict rtol=10^-2
 end
@@ -163,7 +163,7 @@ end
     res = solve(prob, OptimizationOptimJL.BFGS(); callback = callback, maxiters = 300)
     xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
     phi = discretization.phi
-    u_predict = [first(phi([x], res.minimizer)) for x in xs]
+    u_predict = [first(phi([x], res.u)) for x in xs]
     u_real = [1 / x^2 for x in xs]
     @test u_real≈u_predict rtol=10^-2
 end

test/NNPDE_tests.jl

@@ -40,15 +40,15 @@ function test_ode(strategy_)
     prob = discretize(pde_system, discretization)
 
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.1); maxiters = 1000)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.01); maxiters = 500)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.001); maxiters = 500)
     phi = discretization.phi
     analytic_sol_func(t) = exp(-(t^2) / 2) / (1 + t + t^3) + t^2
     ts = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
     u_real = [analytic_sol_func(t) for t in ts]
-    u_predict = [first(phi(t, res.minimizer)) for t in ts]
+    u_predict = [first(phi(t, res.u)) for t in ts]
     @test u_predict≈u_real atol=0.1
 end
 
@@ -183,7 +183,7 @@ function test_2d_poisson_equation(chain_, strategy_)
     xs, ys = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
     analytic_sol_func(x, y) = (sin(pi * x) * sin(pi * y)) / (2pi^2)
 
-    u_predict = reshape([first(phi([x, y], res.minimizer)) for x in xs for y in ys],
+    u_predict = reshape([first(phi([x, y], res.u)) for x in xs for y in ys],
                         (length(xs), length(ys)))
     u_real = reshape([analytic_sol_func(x, y) for x in xs for y in ys],
                      (length(xs), length(ys)))
@@ -431,14 +431,14 @@ end
     @named pde_system = PDESystem(eq, bcs, domains, [θ], [u])
     prob = discretize(pde_system, discretization)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.1); maxiters = 1000)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.01); maxiters = 500)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.001); maxiters = 500)
     phi = discretization.phi
     analytic_sol_func(t) = exp(-(t^2) / 2) / (1 + t + t^3) + t^2
     ts = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
     u_real = [analytic_sol_func(t) for t in ts]
-    u_predict = [first(phi(t, res.minimizer)) for t in ts]
+    u_predict = [first(phi(t, res.u)) for t in ts]
     @test u_predict≈u_real atol=0.1
 end

test/NNPDE_tests_gpu_Lux.jl

@@ -52,7 +52,7 @@ const gpud = gpu_device()
     analytic_sol_func(t) = exp(-(t^2) / 2) / (1 + t + t^3) + t^2
     ts = [infimum(d.domain):(dt / 10):supremum(d.domain) for d in domains][1]
     u_real = [analytic_sol_func(t) for t in ts]
-    u_predict = [first(Array(phi([t], res.minimizer))) for t in ts]
+    u_predict = [first(Array(phi([t], res.u))) for t in ts]
     @test u_predict≈u_real atol=0.2
 end
 
@@ -86,12 +86,12 @@ end
     discretization = PhysicsInformedNN(chain, strategy; init_params = ps)
     prob = discretize(pdesys, discretization)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.01); maxiters = 1000)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.001); maxiters = 1000)
     phi = discretization.phi
     u_exact = (t, x) -> exp.(-t) * cos.(x)
     ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
-    u_predict = reshape([first(Array(phi([t, x], res.minimizer))) for t in ts for x in xs],
+    u_predict = reshape([first(Array(phi([t, x], res.u))) for t in ts for x in xs],
                         (length(ts), length(xs)))
     u_real = reshape([u_exact(t, x) for t in ts for x in xs], (length(ts), length(xs)))
     diff_u = abs.(u_predict .- u_real)
@@ -130,12 +130,12 @@ end
     discretization = PhysicsInformedNN(chain, strategy; init_params = ps)
     prob = discretize(pdesys, discretization)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.1); maxiters = 2000)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.01); maxiters = 2000)
     phi = discretization.phi
     u_exact = (t, x) -> exp(-t) * cos(x)
     ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
-    u_predict = reshape([first(Array(phi([t, x], res.minimizer))) for t in ts for x in xs],
+    u_predict = reshape([first(Array(phi([t, x], res.u))) for t in ts for x in xs],
                         (length(ts), length(xs)))
     u_real = reshape([u_exact(t, x) for t in ts for x in xs], (length(ts), length(xs)))
     diff_u = abs.(u_predict .- u_real)
@@ -184,12 +184,12 @@ end
     @named pde_system = PDESystem(eq, bcs, domains, [t, x, y], [u(t, x, y)])
     prob = discretize(pde_system, discretization)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.01); maxiters = 2500)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = Optimization.solve(prob, OptimizationOptimisers.Adam(0.001); maxiters = 2500)
     phi = discretization.phi
     ts, xs, ys = [infimum(d.domain):0.1:supremum(d.domain) for d in domains]
     u_real = [analytic_sol_func(t, x, y) for t in ts for x in xs for y in ys]
-    u_predict = [first(Array(phi([t, x, y], res.minimizer))) for t in ts for x in xs
+    u_predict = [first(Array(phi([t, x, y], res.u))) for t in ts for x in xs
                 for y in ys]
 
     @test u_predict≈u_real rtol=0.2

test/adaptive_loss_tests.jl

@@ -60,7 +60,7 @@ function test_2d_poisson_equation_adaptive_loss(adaptive_loss; seed = 60, maxite
         return false
     end
     res = solve(prob, OptimizationOptimisers.Adam(0.03); maxiters = maxiters, callback = callback)
-    u_predict = reshape([first(phi([x, y], res.minimizer)) for x in xs for y in ys],
+    u_predict = reshape([first(phi([x, y], res.u)) for x in xs for y in ys],
                         (length(xs), length(ys)))
     diff_u = abs.(u_predict .- u_real)
     total_diff = sum(diff_u)

test/additional_loss_tests.jl

@@ -43,7 +43,7 @@ using ComponentArrays
         function inner_f(x, θ)
             dx * phi(x, θ) .- 1
         end
-        prob1 = IntegralProblem(inner_f, lb, ub, θ)
+        prob1 = IntegralProblem(inner_f, (lb, ub), θ)
         norm2 = solve(prob1, HCubatureJL(), reltol = 1e-8, abstol = 1e-8, maxiters = 10)
         abs(norm2[1])
     end
@@ -63,7 +63,7 @@ using ComponentArrays
         return false
     end
     res = solve(prob, OptimizationOptimJL.LBFGS(), maxiters = 400, callback = cb_)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = solve(prob, OptimizationOptimJL.BFGS(), maxiters = 2000, callback = cb_)
     C = 142.88418699042
     analytic_sol_func(x) = C * exp((1 / (2 * _σ^2)) * (2 * α * x^2 - β * x^4))
@@ -88,7 +88,7 @@ using ComponentArrays
         return false
     end
     res = solve(prob, OptimizationOptimJL.LBFGS(), maxiters = 400, callback = cb_)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = solve(prob, OptimizationOptimJL.BFGS(), maxiters = 2000, callback = cb_)
     C = 142.88418699042
     analytic_sol_func(x) = C * exp((1 / (2 * _σ^2)) * (2 * α * x^2 - β * x^4))
@@ -170,7 +170,7 @@ end
                                             Float64[])
 
     res = solve(prob, OptimizationOptimJL.BFGS(); maxiters = 6000)
-    p_ = res.minimizer[(end - 2):end]
+    p_ = res.u[(end - 2):end]
     @test sum(abs2, p_[1] - 10.00) < 0.1
     @test sum(abs2, p_[2] - 28.00) < 0.1
     @test sum(abs2, p_[3] - (8 / 3)) < 0.1
@@ -189,7 +189,7 @@ end
     sym_prob = NeuralPDE.symbolic_discretize(pde_system, discretization)
     sym_prob.loss_functions.full_loss_function(sym_prob.flat_init_params, nothing)
     res = solve(prob, OptimizationOptimJL.BFGS(); maxiters = 6000)
-    p_ = res.minimizer[(end - 2):end]
+    p_ = res.u[(end - 2):end]
     @test sum(abs2, p_[1] - 10.00) < 0.1
     @test sum(abs2, p_[2] - 28.00) < 0.1
     @test sum(abs2, p_[3] - (8 / 3)) < 0.1
@@ -225,7 +225,7 @@ end
     phi(xs, flat_init_params)
     additional_loss_(phi, flat_init_params, nothing)
     res = solve(prob, OptimizationOptimisers.Adam(0.01), maxiters = 500)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = solve(prob, OptimizationOptimJL.BFGS(), maxiters = 500)
     @test phi(xs, res.u)≈aproxf_(xs) rtol=0.01
 end

test/direct_function_tests.jl

@@ -35,7 +35,7 @@ Random.seed!(110)
     @named pde_system = PDESystem(eq, bc, domain, [x], [u(x)])
     prob = discretize(pde_system, discretization)
     res = solve(prob, OptimizationOptimisers.Adam(0.05), maxiters = 1000)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = solve(prob, OptimizationOptimJL.BFGS(initial_stepnorm = 0.01), maxiters = 500)
     @test discretization.phi(xs', res.u)≈func(xs') rtol=0.01
 end
@@ -62,7 +62,7 @@ end
     @named pde_system = PDESystem(eq, bc, domain, [x], [u(x)])
     prob = discretize(pde_system, discretization)
     res = solve(prob, OptimizationOptimisers.Adam(0.01), maxiters = 500)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = solve(prob, OptimizationOptimJL.BFGS(), maxiters = 1000)
     dx = 0.01
     xs = collect(x0:dx:x_end)
@@ -95,14 +95,14 @@ end
     symprob = NeuralPDE.symbolic_discretize(pde_system, discretization)
     symprob.loss_functions.full_loss_function(symprob.flat_init_params, nothing)
     res = solve(prob, OptimizationOptimisers.Adam(0.01), maxiters = 500)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = solve(prob, OptimizationOptimJL.BFGS(), maxiters = 1000)
-    prob = remake(prob, u0 = res.minimizer)
+    prob = remake(prob, u0 = res.u)
     res = solve(prob, OptimizationOptimJL.BFGS(), maxiters = 500)
     phi = discretization.phi
     xs = collect(x0:0.1:x_end)
     ys = collect(y0:0.1:y_end)
-    u_predict = reshape([first(phi([x, y], res.minimizer)) for x in xs for y in ys],
+    u_predict = reshape([first(phi([x, y], res.u)) for x in xs for y in ys],
                         (length(xs), length(ys)))
     u_real = reshape([func(x, y) for x in xs for y in ys], (length(xs), length(ys)))
     diff_u = abs.(u_predict .- u_real)