# NeuralPDE.jl: Scientific Machine Learning for Partial Differential Equations

NeuralPDE.jl NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using physics-informed neural networks (PINNs).

## 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
• 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
• Compatability with Flux.jl and Lux.jl for all of the GPU-powered machine learning layers available from those libraries.
• Compatability with NeuralOperators.jl for mixing DeepONets and other neural operators (Fourier Neural Operators, Graph Neural Operators, etc.) with physics-informed loss functions

## Citation

@misc{https://doi.org/10.48550/arxiv.2107.09443,
}