USNCCM Poster - Interface learning: Towards seamless integration of multi-X models
Channel: Shady Emad
Date: 2021-07-28

This is part of the poster session presented virtually at the 16th U.S. National Congress on Computational Mechanics (USNCCM16).

A longer presentation is available at the following link: 
https://youtu.be/c3_c6allLik

Abstract:

A multitude of natural and engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, multiple physical closure laws, and even multiple disciplines. In a naïve implementation of numerical simulation, the stiffest part dictates the spatial mesh resolution and time stepping requirements, making the solution of such systems computationally daunting. Instead, an ensemble of solvers and modeling approaches with varying levels of complexity can be selected for efficient simulation of multi-scale, multi-physics and multi-fidelity (M3) models. This includes domain decomposition techniques, multi-fidelity solvers, and multi-geometrical abstractions. However, effective communications and information sharing among heterogeneous computational entities have to be accomplished to guarantee solution convergence and reduce idle times. To this end, we propose a series of interface learning (IL) paradigms to pave the way for seamless integration of M3 models using hybrid analysis and modeling (HAM) techniques.

We exploit a robust HAM 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 focus on a multi-fidelity formulation targeting domain decomposition type problems that consist of multiple zones with different characteristics as well as multi-physics systems where different levels of solvers are devoted to coupled physical phenomena. A key aspect of the zonal multi-fidelity approach is its ability to handle intrinsic heterogeneous physical properties, varying geometries, and underlying governing dynamics. 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 and prolongation. The proposed IL 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.

Relevant papers:
1- Shady E. Ahmed, Omer San, Kursat Kara, Rami Younis, and Adil Rasheed. "Interface learning of multiphysics and multiscale systems." Physical Review E 102, no. 5 (2020): 053304.

2- Shady E. Ahmed, Omer San, Kursat Kara, Rami Younis, and Adil Rasheed. "Multifidelity computing for coupling full and reduced order models." Plos one 16, no. 2 (2021): e0246092.

3- Suraj Pawar, Shady E. Ahmed, and Omer San. "Interface learning in fluid dynamics: Statistical inference of closures within micro–macro-coupling models." Physics of Fluids 32, no. 9 (2020): 091704.