NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using physics-informed neural networks (PINNs). This package utilizes neural stochastic differential equations to solve PDEs at a greatly increased generality compared with classical methods.
Assuming that you already have Julia correctly installed, it suffices to install NeuralPDE.jl in the standard way, that is, by typing ] add NeuralPDE. Note:
to exit the Pkg REPL-mode, just press Backspace or Ctrl + C.
For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.
- Physics-Informed Neural Networks for ODE, SDE, RODE, and PDE solving
- Ability to define extra loss functions to mix xDE solving with data fitting (scientific machine learning)
- Automated construction of Physics-Informed loss functions from a high level symbolic interface
- Sophisticated techniques like quadrature training strategies, adaptive loss functions, and neural adapters to accelerate training
- Integrated logging suite for handling connections to TensorBoard
- Handling of (partial) integro-differential equations and various stochastic equations
- Specialized forms for solving
ODEProblems with neural networks - Compatibility with Flux.jl and Lux.jl for all of the GPU-powered machine learning layers available from those libraries.
- Compatibility with NeuralOperators.jl for mixing DeepONets and other neural operators (Fourier Neural Operators, Graph Neural Operators, etc.) with physics-informed loss functions
using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimisers
import ModelingToolkit: Interval, infimum, supremum
@parameters x y
@variables u(..)
Dxx = Differential(x)^2
Dyy = Differential(y)^2
# 2D PDE
eq = Dxx(u(x, y)) + Dyy(u(x, y)) ~ -sin(pi * x) * sin(pi * y)
# Boundary conditions
bcs = [u(0, y) ~ 0.0, u(1, y) ~ 0,
u(x, 0) ~ 0.0, u(x, 1) ~ 0]
# Space and time domains
domains = [x ∈ Interval(0.0, 1.0),
y ∈ Interval(0.0, 1.0)]
# Discretization
dx = 0.1
# Neural network
dim = 2 # number of dimensions
chain = Lux.Chain(Dense(dim, 16, Lux.σ), Dense(16, 16, Lux.σ), Dense(16, 1))
discretization = PhysicsInformedNN(chain, QuadratureTraining())
@named pde_system = PDESystem(eq, bcs, domains, [x, y], [u(x, y)])
prob = discretize(pde_system, discretization)
callback = function (p, l)
println("Current loss is: $l")
return false
end
res = Optimization.solve(prob, ADAM(0.1); callback = callback, maxiters = 4000)
prob = remake(prob, u0 = res.minimizer)
res = Optimization.solve(prob, ADAM(0.01); callback = callback, maxiters = 2000)
phi = discretization.phiAnd some analysis:
xs, ys = [infimum(d.domain):(dx / 10):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],
(length(xs), length(ys)))
u_real = reshape([analytic_sol_func(x, y) for x in xs for y in ys],
(length(xs), length(ys)))
diff_u = abs.(u_predict .- u_real)
using Plots
p1 = plot(xs, ys, u_real, linetype = :contourf, title = "analytic");
p2 = plot(xs, ys, u_predict, linetype = :contourf, title = "predict");
p3 = plot(xs, ys, diff_u, linetype = :contourf, title = "error");
plot(p1, p2, p3)
If you use NeuralPDE.jl in your research, please cite this paper:
@article{zubov2021neuralpde,
title={NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations},
author={Zubov, Kirill and McCarthy, Zoe and Ma, Yingbo and Calisto, Francesco and Pagliarino, Valerio and Azeglio, Simone and Bottero, Luca and Luj{\'a}n, Emmanuel and Sulzer, Valentin and Bharambe, Ashutosh and others},
journal={arXiv preprint arXiv:2107.09443},
year={2021}
}