@Article{CiCP-37-942,
author = {Chen , MoDing , ZhaoJiao , YulingLu , XiliangWu , Peiying and Yang , Jerry Zhijian},
title = {Convergence Analysis of PINNs with Over-Parameterization},
journal = {Communications in Computational Physics},
year = {2025},
volume = {37},
number = {4},
pages = {942--974},
abstract = {<p style="text-align: justify;">Recently, physics-informed neural networks (PINNs) have been shown to
be a simple and efficient method for solving PDEs empirically. However, the numerical analysis of PINNs is still incomplete, especially why over-parameterized PINNs
work remains unknown. This paper presents the first convergence analysis of the over-parameterized PINNs for the Laplace equations with Dirichlet boundary conditions.
We demonstrate that the convergence rate can be controlled by the weight norm, regardless of the number of parameters in the network.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2023-0101},
url = {http://global-sci.org/intro/article_detail/cicp/24028.html}
}