Residual point generation methods using Voronoi diagrams for physics-informed neural networks-Reference-Cited by-同舟云学术

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Residual point generation methods using Voronoi diagrams for physics-informed neural networks

Published:2025-09 Issue: Volume:537 Page:114122
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Sun Bingteng^ORCID,Li Siyi,Xie Yaguang,Du Qiang,Zhu JunQiang

Funder

National Major Science and Technology Projects of China

National Natural Science Foundation of China

Taishan Scholar Project of Shandong Province

National Science and Technology Major Project

Chinese Academy of Sciences

Taishan Scholar Foundation of Shandong Province

Publisher

Elsevier BV

Reference41 articles.

1. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J. Comput. Phys.,2019

2. Automatic differentiation in machine learning: a survey;Baydin;J. Mach. Learn. Res.,2018

3. Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems;Zhang;J. Comput. Phys.,2019

4. Physics informed neural network using finite difference method;Lim,2022

5. Comparison of physics informed neural networks and finite element method solvers for advection-dominated diffusion problems;Sikora;J. Comput. Sci.,2024

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