Utilizing symmetry-enhanced physics-informed neural network to obtain the solution beyond sampling domain for partial differential equations-Reference-Cited by-同舟云学术

Utilizing symmetry-enhanced physics-informed neural network to obtain the solution beyond sampling domain for partial differential equations

Published:2023-11-01 Issue:23 Volume:111 Page:21861-21876
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Li Jie-Ying,Zhang Hui,Liu Ye,Guo Lei-Lei,Zhang Li-Sheng,Zhang Zhi-Yong^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11071-023-08975-w.pdf

Reference41 articles.

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5. Li, J., Chen, J., Li, B.: Gradient-optimized physics-informed neural networks (GOPINNs): a deep learning method for solving the complex modified KdV equation. Nonlinear Dyn. 107, 781–792 (2022)