A Solution Method for Differential Equations Based on Taylor PINN-Reference-Cited by-同舟云学术

A Solution Method for Differential Equations Based on Taylor PINN

Published:2023 Issue: Volume:11 Page:145020-145030
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zhang Yajuan¹,Wang Min²^ORCID,Zhang Fangwei³,Chen Zhenrui²

Affiliation:

1. College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, China

2. Department of Basic Education, Shandong Jiaotong University, Weihai, China

3. School of Maritime, Shandong Jiaotong University, Weihai, China

Funder

Hainan Provincial Natural Science Foundation of China

Project: Provincial High-Level Professional Group “Computer Network Technology,” Hainan Province; and

Project: The Fifth Round of Provincial Characteristic Key Discipline “Computer Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10311559.pdf?arnumber=10311559

Reference41 articles.

1. Bilinear neural network method to obtain the exact analytical solutions of nonlinear partial differential equations and its application to p-gBKP equation

2. Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations;Jagtap

3. Fractal Solitons, Arbitrary Function Solutions, Exact Periodic Wave and Breathers for a Nonlinear Partial Differential Equation by Using Bilinear Neural Network Method

4. Dynamics of three-point boundary value problems with Gudermannian neural networks

5. Neural jump stochastic differential equations;Jia