Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solving

Fuente: arXiv
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Main Authors: Zhou, Cuizhi, Zhu, Kaien
Format: Preprint
Published: 2025
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author Zhou, Cuizhi
Zhu, Kaien
author_facet Zhou, Cuizhi
Zhu, Kaien
contents The solution of partial differential equations (PDES) on irregular domains has long been a subject of significant research interest. In this work, we present an approach utilizing physics-informed neural networks (PINNs) to achieve irregular-to-regular domain mapping. Thus we can use finite difference method and physics-informed convolutional neural networks to solve PDEs on rectangular grids instead of the original irregular boundary. Structured grids on irregular domains are obtained by inverse mapping. We demonstrate PINN's versatile capability to produce customized structured grids tailored to diverse computational requirements, thereby significantly facilitating PDEs solving.
format Preprint
id arxiv_https___arxiv_org_abs_2506_08622
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solving
Zhou, Cuizhi
Zhu, Kaien
Computational Physics
The solution of partial differential equations (PDES) on irregular domains has long been a subject of significant research interest. In this work, we present an approach utilizing physics-informed neural networks (PINNs) to achieve irregular-to-regular domain mapping. Thus we can use finite difference method and physics-informed convolutional neural networks to solve PDEs on rectangular grids instead of the original irregular boundary. Structured grids on irregular domains are obtained by inverse mapping. We demonstrate PINN's versatile capability to produce customized structured grids tailored to diverse computational requirements, thereby significantly facilitating PDEs solving.
title Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solving
topic Computational Physics
url https://arxiv.org/abs/2506.08622