DGNN: A Neural PDE Solver Induced by Discontinuous Galerkin Methods
Machine Learning
2025-03-17 v2 Computational Physics
Abstract
We propose a general framework for the Discontinuous Galerkin-induced Neural Network (DGNN), inspired by the Interior Penalty Discontinuous Galerkin Method (IPDGM). In this approach, the trial space consists of piecewise neural network space defined over the computational domain, while the test function space is composed of piecewise polynomials. We demonstrate the advantages of DGNN in terms of accuracy and training efficiency across several numerical examples, including stationary and time-dependent problems. Specifically, DGNN easily handles high perturbations, discontinuous solutions, and complex geometric domains.
Keywords
Cite
@article{arxiv.2503.10021,
title = {DGNN: A Neural PDE Solver Induced by Discontinuous Galerkin Methods},
author = {Guanyu Chen and Shengze Xu and Dong Ni and Tieyong Zeng},
journal= {arXiv preprint arXiv:2503.10021},
year = {2025}
}