Multifidelity Computing for Coupling Full and Reduced Order Models

TL;DR

This paper introduces a hybrid modeling approach combining physics-based full order models and data-driven reduced order models, using machine learning to improve coupling and error correction in complex transport simulations.

Contribution

It presents a novel multifidelity coupling framework with LSTM-based interface learning for nonlinear advection-diffusion problems.

Findings

Effective ROM-FOM coupling demonstrated in transport simulations

LSTM networks improve interface error correction

Framework applicable to broad class of multiphysics problems

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.