fOGA: Orthogonal Greedy Algorithm for Fractional Laplace Equations

Fuente: arXiv
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Autori principali: Shan, Ruitong, Lee, Young Ju, Jia, Jiwei
Natura: Preprint
Pubblicazione: 2024
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author Shan, Ruitong
Lee, Young Ju
Jia, Jiwei
author_facet Shan, Ruitong
Lee, Young Ju
Jia, Jiwei
contents In this paper, we explore the finite difference approximation of the fractional Laplace operator in conjunction with a neural network method for solving it. We discretized the fractional Laplace operator using the Riemann-Liouville formula relevant to fractional equations. A shallow neural network was constructed to address the discrete fractional operator, coupled with the OGA algorithm. To validate the feasibility of our approach, we conducted numerical experiments, testing both the Laplace operator and the fractional Laplace operator, yielding favorable convergence results.
format Preprint
id arxiv_https___arxiv_org_abs_2409_16551
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle fOGA: Orthogonal Greedy Algorithm for Fractional Laplace Equations
Shan, Ruitong
Lee, Young Ju
Jia, Jiwei
Numerical Analysis
In this paper, we explore the finite difference approximation of the fractional Laplace operator in conjunction with a neural network method for solving it. We discretized the fractional Laplace operator using the Riemann-Liouville formula relevant to fractional equations. A shallow neural network was constructed to address the discrete fractional operator, coupled with the OGA algorithm. To validate the feasibility of our approach, we conducted numerical experiments, testing both the Laplace operator and the fractional Laplace operator, yielding favorable convergence results.
title fOGA: Orthogonal Greedy Algorithm for Fractional Laplace Equations
topic Numerical Analysis
url https://arxiv.org/abs/2409.16551