Multi-Scale Message Passing Neural PDE Solvers
Abstract
We propose a novel multi-scale message passing neural network algorithm for learning the solutions of time-dependent PDEs. Our algorithm possesses both temporal and spatial multi-scale resolution features by incorporating multi-scale sequence models and graph gating modules in the encoder and processor, respectively. Benchmark numerical experiments are presented to demonstrate that the proposed algorithm outperforms baselines, particularly on a PDE with a range of spatial and temporal scales.
Cite
Text
Equer et al. "Multi-Scale Message Passing Neural PDE Solvers." ICLR 2023 Workshops: Physics4ML, 2023.Markdown
[Equer et al. "Multi-Scale Message Passing Neural PDE Solvers." ICLR 2023 Workshops: Physics4ML, 2023.](https://mlanthology.org/iclrw/2023/equer2023iclrw-multiscale/)BibTeX
@inproceedings{equer2023iclrw-multiscale,
title = {{Multi-Scale Message Passing Neural PDE Solvers}},
author = {Equer, Léonard and Rusch, T. Konstantin and Mishra, Siddhartha},
booktitle = {ICLR 2023 Workshops: Physics4ML},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/equer2023iclrw-multiscale/}
}