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• W3128467383 abstract "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." @default.
• W3128467383 created "2021-02-15" @default.
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• W3128467383 date "2021-02-11" @default.
• W3128467383 modified "2023-10-07" @default.
• W3128467383 title "Multifidelity computing for coupling full and reduced order models" @default.
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• W3128467383 doi "https://doi.org/10.1371/journal.pone.0246092" @default.
• W3128467383 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7877632" @default.
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• W3128467383 hasPublicationYear "2021" @default.
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