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

DOI10.1007/s10444-023-10065-9arXiv2107.07871OpenAlexW3184908771MaRDI QIDQ6171723

Tarje Nissen-Meyer, Andrew Markham, Ben Moseley

Publication date: 14 August 2023

Published in: Advances in Computational Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.07871

zbMATH Keywords

domain decompositiondifferential equationsparallel computingmulti-scale modellingforward modellingphysics-informed neural networks

Mathematics Subject Classification ID

General topics in artificial intelligence (68T01) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)

Related Items (10)

Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method ⋮ On the use of neural networks for full waveform inversion ⋮ Pre-training strategy for solving evolution equations based on physics-informed neural networks ⋮ An overlapping domain decomposition method for the solution of parametric elliptic problems via proper generalized decomposition ⋮ Finite element interpolated neural networks for solving forward and inverse problems ⋮ Learning Specialized Activation Functions for Physics-Informed Neural Networks ⋮ Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations ⋮ Distributed PINN for Linear Elasticity — A Unified Approach for Smooth, Singular, Compressible and Incompressible Media ⋮ Hard enforcement of physics-informed neural network solutions of acoustic wave propagation ⋮ Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations

Cites Work

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