@@ -69,7 +69,7 @@ function loss(cord, θ)
6969 ch2 .- phi(cord, res.u)
7070end
7171
72- strategy = NeuralPDE.QuadratureTraining(; reltol = 1e-6)
72+ strategy = NeuralPDE.QuadratureTraining(; reltol = 1e-6, abstol = 1e-3 )
7373
7474prob_ = NeuralPDE.neural_adapter(loss, init_params2, pde_system, strategy)
7575res_ = Optimization.solve(prob_, OptimizationOptimisers.Adam(5e-3); maxiters = 10000)
@@ -173,7 +173,7 @@ for i in 1:count_decomp
173173 bcs_ = create_bcs(domains_[1].domain, phi_bound)
174174 @named pde_system_ = PDESystem(eq, bcs_, domains_, [x, y], [u(x, y)])
175175 push!(pde_system_map, pde_system_)
176- strategy = NeuralPDE.QuadratureTraining(; reltol = 1e-6)
176+ strategy = NeuralPDE.QuadratureTraining(; reltol = 1e-6, abstol = 1e-3 )
177177
178178 discretization = NeuralPDE.PhysicsInformedNN(chains[i], strategy;
179179 init_params = init_params[i])
@@ -243,10 +243,10 @@ callback = function (p, l)
243243end
244244
245245prob_ = NeuralPDE.neural_adapter(losses, init_params2, pde_system_map,
246- NeuralPDE.QuadratureTraining(; reltol = 1e-6))
246+ NeuralPDE.QuadratureTraining(; reltol = 1e-6, abstol = 1e-3 ))
247247res_ = Optimization.solve(prob_, OptimizationOptimisers.Adam(5e-3); maxiters = 5000)
248248prob_ = NeuralPDE.neural_adapter(losses, res_.u, pde_system_map,
249- NeuralPDE.QuadratureTraining(; reltol = 1e-6))
249+ NeuralPDE.QuadratureTraining(; reltol = 1e-6, abstol = 1e-3 ))
250250res_ = Optimization.solve(prob_, OptimizationOptimisers.Adam(5e-3); maxiters = 5000)
251251
252252phi_ = PhysicsInformedNN(chain2, strategy; init_params = res_.u).phi
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