Multifidelity modeling for physics-informed neural networks (PINNs)

DOI10.1016/j.jcp.2021.110844OpenAlexW3211801636MaRDI QIDQ2134766

Michael Penwarden, Shandian Zhe, Akil C. Narayan, Robert M. Kirby

Publication date: 3 May 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2106.13361

zbMATH Keywords

reduced-order modelingmultifidelitysurrogate modelingphysics-informed neural networks (PINNs)

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Miscellaneous topics in partial differential equations (35Rxx)

Related Items

Improved deep neural networks with domain decomposition in solving partial differential equations, Multifidelity deep operator networks for data-driven and physics-informed problems, A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs, An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations

Uses Software

• FPINNs
• PPINN
• XPINNs
• hp-VPINNs

Cites Work

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