Multifidelity computing for coupling full and reduced order models

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 var...

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Bibliographic Details
Published in:PloS one 2021-02, Vol.16 (2), p.e0246092-e0246092
Main Authors: Ahmed, Shady E, San, Omer, Kara, Kursat, Younis, Rami, Rasheed, Adil
Format: Article
Language:English
Subjects:
Accuracy
Actuation
Actuators
Aerospace engineering
BASIC BIOLOGICAL SCIENCES
Biology and Life Sciences
Computational fluid dynamics
Computational mathematics
Computer and Information Sciences
Computer programming
Computer simulation
Computer-generated environments
Cybernetics
Data retrieval
density
Distance learning
Divergence
Earth Sciences
Efficiency
Engineering and Technology
flow field
Fluid flow
Interfaces
Interpolation
Laminar flow
Machine learning
Mathematical models
Methods
Neural networks
Physical Sciences
Physics
Reduced order models
Simulation
simulation and modeling
twins
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0246092