TensorFlow 2 implementation of Physics-Informed Neural Networks (PINNs) based on Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations. Implemented equations are
- Heat,
- Wave,
- Reaction-Diffusion,
- Stationary Advection-Diffusion,
- Poisson,
- Schrodinger's,
- Burger's,
- Klein Gordon,
- and Transport.
Examples of each PINN are available in the notebooks.
Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations
This repository contains the code base for the paper "Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations," accepted at IJCAI 2024 (see on arXiv). Switch to the ijcai branch to access training scripts.
@article{dashtbayaz2024physicsinformed,
title = {Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations},
author = {Nima Hosseini Dashtbayaz and Ghazal Farhani and Boyu Wang and Charles X. Ling},
year = {2024},
journal = {arXiv preprint arXiv: 2405.01680}
}