Two-scale Neural Networks for Partial Differential Equations with Small Parameters
Numerical Analysis
2024-10-15 v3 Machine Learning
Numerical Analysis
Computational Physics
Abstract
We propose a two-scale neural network method for solving partial differential equations (PDEs) with small parameters using physics-informed neural networks (PINNs). We directly incorporate the small parameters into the architecture of neural networks. The proposed method enables solving PDEs with small parameters in a simple fashion, without adding Fourier features or other computationally taxing searches of truncation parameters. Various numerical examples demonstrate reasonable accuracy in capturing features of large derivatives in the solutions caused by small parameters.
Cite
@article{arxiv.2402.17232,
title = {Two-scale Neural Networks for Partial Differential Equations with Small Parameters},
author = {Qiao Zhuang and Chris Ziyi Yao and Zhongqiang Zhang and George Em Karniadakis},
journal= {arXiv preprint arXiv:2402.17232},
year = {2024}
}