E-PINN: extended physics informed neural network for the forward and inverse problems of high-order nonlinear integro-differential equations

Abstract

Physics informed neural network (PINN) is a new deep learning paradigm, which embeds the physical information delineated by PDEs in the loss function and optimizes the weights in the neural network. Based on PINN, an extended PINN(E-PINN) is proposed, which is a mixture of the polynomial function approximation method and PINN's learning framework. A preprocess layer is added before the classical PINN, using Legendre polynomials as the polynomial basis function. Therefore, E-PINN not only has the excellent approximation ability of the polynomial basis function, but also inherits the learning framework of the neural network method. In numerical experiments, the proposed E-PINN algorithms have high accuracy in solving 1D, 2D high-order nonlinear Fredholm equations and equations system, including the forward and inverse problems.

Keywords:

• E-PINN
• nonlinear
• integro-differential equation
• inverse problem

2020 Mathematics Subject Classifications:

• 65R32
• 65R20

Disclosure statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Additional information

Funding

This research is supported by the Natural Science Foundation of Zhejiang Province, China (No. LY21A010011). Author contribution s Hong-Ming Zhang: Software, Investigation, Coding, Data curation, Writing–original draft. Xin-Ping Shao: Conceptualization, Methodology, Supervision, Project administration, Coding, Writing–review & editing. Zheng-Fang Zhang: Investigation, Supervision, Data curation, Writing–review & editing. Ming-Yan He: Writing–review & editing.