Approximating Partial Differential Equations with Physics-Informed Legendre Multiwavelets CNN

Approximating Partial Differential Equations with Physics-Informed Legendre Multiwavelets CNN

The purpose of this paper is to leverage the advantages of physics-informed neural network (PINN) and convolutional neural network (CNN) by using Legendre multiwavelets (LMWs) as basis functions to approximate partial differential equations (PDEs). We call this method Physics-Informed Legendre Multi...

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Bibliographic Details
Main Authors: Yahong Wang, Wenmin Wang, Cheng Yu, Hongbo Sun, Ruimin Zhang
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Fractal and Fractional
Subjects:
partial differential equations
Legendre multiwavelets
physics-informed neural network
convolutional neural network
Online Access:https://www.mdpi.com/2504-3110/8/2/91

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https://www.mdpi.com/2504-3110/8/2/91

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