NeuralPDE.jl
NeuralPDE.jl copied to clipboard
SciML
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Hi, following the new tutorial I moved my GPU net from Flux to Lux. Unfortunately now I cannot use BFGS for the optimization. The code I use is the following:...
Hi, I translated my code to the new version of NeuralPDE, and I saw that the GPU example now uses Lux instead of Flux. Unfortunately, while in the previous version...
Hi, I want to solve a fairly simple PDE: ``` # Parameters τ_min= 0. τ_max = 1.0 τ_span = (τ_min,τ_max) ω_min = -2. ω_max = 2. #Grid if needed dω...
using [example](https://neuralpde.sciml.ai/dev/tutorials/gpu/), without initial boundary value. just modify "**_bcs=[ ]_**" ``` res = Optimization.solve(prob,Adam(0.01);callback = callback,maxiters=2500) ``` then it will show ``` MethodError: no method matching zero(::Type{Any}) Closest candidates are:...
$M S E=A_1 M S E_{0}+A_2 M S E_{b}+A_3 M S E_{f}$ How to optimize the loss weight: A1 A2 A3, via NeuralPDE?
```julia using NeuralPDE, Lux, Optimization, OptimizationOptimJL import ModelingToolkit: Interval @parameters t, x, C @variables u(..) Dxx = Differential(x)^2 Dtt = Differential(t)^2 Dt = Differential(t) eq = Dtt(u(t,x)) ~ C^2*Dxx(u(t,x)) bcs...
Implementation Physics-informed neural operator method for solve parametric Ordinary Differential Equations (ODE) use DeepOnet. https://github.com/SciML/NeuralPDE.jl/issues/575 Checklist - [x] pino ode - [x] family ode by parameter - [x] physics informed...
I am trying to solve the DAE system. From the results, although the loss function is very small, the status of the optimization solver is a success, but the results...
