  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.
