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.
Highlights
Numerical simulations are the workhorse for the design, testing, and implementation of scientific infrastructure and engineering applications
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
We provide an interface learning approach via ROM-FOM coupling for multifidelity simulation environments
Summary
Numerical simulations are the workhorse for the design, testing, and implementation of scientific infrastructure and engineering applications. The ROM solution can be used to infer the flow conditions at the interface so that a FOM solver can be efficiently employed for the sub-domains of interest.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have