Multifidelity deep operator networks for data-driven and physics-informed problems

DOI10.1016/j.jcp.2023.112462arXiv2204.09157OpenAlexW4386366778MaRDI QIDQ6048427

Mauro Perego, Amanda A. Howard, Panos Stinis, George Em. Karniadakis

Publication date: 10 October 2023

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

Full work available at URL: https://arxiv.org/abs/2204.09157

zbMATH Keywords

multifidelityphysics-informed machine learningoperator learningneural operatorice-sheet dynamics

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (1)

Branched latent neural maps

Cites Work

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