Two neural-network-based methods for solving obstacle problems
Numerical Analysis
2022-08-10 v1 Numerical Analysis
Analysis of PDEs
Abstract
Two neural-network-based numerical schemes are proposed to solve the classical obstacle problems. The schemes are based on the universal approximation property of neural networks, and the cost functions are taken as the energy minimization of the obstacle problems. We rigorously prove the convergence of the two schemes and derive the convergence rates with the number of neurons . In the simulations, we use two example problems (1-D & 2-D) to verify the convergence rate of the methods and the quality of the results.
Cite
@article{arxiv.2111.01761,
title = {Two neural-network-based methods for solving obstacle problems},
author = {Xinyue Evelyn Zhao and Wenrui Hao and Bei Hu},
journal= {arXiv preprint arXiv:2111.01761},
year = {2022}
}