NeuralPDE.jl
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SciML
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
GPU Compatibility Issue: Compilation Error with Complex-Valued Data in LuxCUDA Broadcasting Kernel
# Bug Description ## Summary When attempting to solve a neural network optimization problem on a GPU using Lux and LuxCUDA packages in Julia, a GPU compilation error occurs. ##...
**Describe the bug 🐞** Solving a simple bvp with AMDGPU backend breaks down in the Optimization.solve step with an error **Expected behavior** Optimizer should run smoothly (as it is the...
## Checklist - [ ] Appropriate tests were added - [ ] Any code changes were done in a way that does not break public API - [ ] All...
Hi, I am getting the following error when trying to solve the inverse problem (learning \lambda) using NeuralPDE.jl: "`KeyError: key λ not found`" and it points towards the line `@named...
## Checklist - [ ] Appropriate tests were added - [ ] Any code changes were done in a way that does not break public API - [ ] All...
**Describe the bug 🐞** The `PhysicsInformedNN` discretizer has two arguments `param_estim` and `additional_loss` for parameter estimation. The `additional_loss` is a function which accepts three arguments, i.e., `additional_loss(phi, θ, p)` where...
## Checklist - [ ] Appropriate tests were added - [ ] Any code changes were done in a way that does not break public API - [ ] All...
Implement vector output for PINO ODE draft test case: ```julia #vector outputs @testset "Example ode system: du1 = cos(p * t); du2 = sin(p * t)" begin equation = (u,...
Added a custom broadcast function for GPU with KA.jl. But it gives me an error while running solve. MWE: ```julia using Random, NeuralPDE using OrdinaryDiffEq using Lux, OptimizationOptimisers using LuxCUDA,...