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Hybrid physics-machine learning models are increasingly being used insimulations of transport processes. Many complex multiphysics systems relevantto scientific and engineering applications include multiple spatiotemporalscales and comprise a multifidelity problem sharing an interface betweenvarious formulations or heterogeneous computational entities. To this end, wepresent a robust hybrid analysis and modeling approach combining aphysics-based full order model (FOM) and a data-driven reduced order model(ROM) to form the building blocks of an integrated approach among mixedfidelity descriptions toward predictive digital twin technologies. At theinterface, we introduce a long short-term memory network to bridge these highand low-fidelity models in various forms of interfacial error correction orprolongation. The proposed interface learning approaches are tested as a newway to address ROM-FOM coupling problems solving nonlinear advection-diffusionflow situations with a bifidelity setup that captures the essence of a broadclass of transport processes.

Multifidelity Computing for Coupling Full and Reduced Order Models