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

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

doi:10.1016/j.jcp.2023.112462

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

Journal Article · Fri Sep 01 00:00:00 EDT 2023 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2023.112462· OSTI ID:2008415

^[1]; Perego, Mauro ^[2]; Karniadakis, George Em ^[3]; ^[1]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Brown Univ., Providence, RI (United States)

Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires a large amount of expensive, high-fidelity data, either from experiments or simulations. In this work, we present a composite Deep Operator Network (DeepONet) for learning using two datasets with different levels of fidelity to accurately learn complex operators when sufficient high-fidelity data is not available. Additionally, we demonstrate that the presence of low-fidelity data can improve the predictions of physics-informed learning with DeepONets. We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)

Grant/Contract Number:: AC05-76RL01830; NA0003525

OSTI ID:: 2008415

Alternate ID(s):: OSTI ID: 2369182

Report Number(s):: PNNL-SA--172145

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 493; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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