Multifidelity computing for coupling full and reduced order models

Ahmed, Shady E.; San, Omer; Kara, Kursat; Younis, Rami; Rasheed, Adil

doi:10.1371/journal.pone.0246092

Title: Multifidelity computing for coupling full and reduced order models

Journal Article · Thu Feb 11 00:00:00 EST 2021 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0246092· OSTI ID:1816456

^[1];

^[1]; Younis, Rami ^[2]; Rasheed, Adil ^[3]

Oklahoma State Univ., Stillwater, OK (United States). School of Mechanical & Aerospace Engineering
Univ. of Tulsa, Tulsa, OK (United States). The McDougall School of Petroleum Engineering
Norwegian Univ. of Science and Technology, Trondheim (Norway). Dept. of Engineering Cybernetics; SINTEF Digital, Trondheim (Norway). Dept. of Mathematics and Cybernetics

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.

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Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Norwegian Research Council

Grant/Contract Number:: SC0019290; 268044/E20

OSTI ID:: 1816456

Journal Information:: PLoS ONE, Vol. 16, Issue 2; ISSN 1932-6203

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Multifidelity computing for coupling full and reduced order models

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