Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm for solving problems relating to differential equations. Compared to classical numerical methods, PINNs have several advantages, for example their ability to provide mesh-free solutions of differential equations...

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Bibliographic Details
Published in:	Advances in computational mathematics Vol. 49; no. 4; p. 62
Main Authors:	Moseley, Ben, Markham, Andrew, Nissen-Meyer, Tarje
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.08.2023 Springer Nature B.V
Subjects:	Basis functions Bias Complexity Computational Mathematics and Numerical Analysis Computational Science and Engineering Differential equations Domain decomposition methods Finite element method Mathematical analysis Mathematical and Computational Biology Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Neural networks Numerical methods Optimization Physics Problem solving Visualization 68T01 Parallel computing Forward modelling Differential equations Multi-scale modelling 65M99 Domain decomposition Physics-informed neural networks
ISSN:	1019-7168, 1572-9044
Online Access:	Get full text
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