Work

Learning-based reduced order model stabilization for partial differential equations: Application to the coupled Burgers' equation

Year: 2016

Type: conference-paper

Abstract: We present results on stabilization for reduced order models (ROM) of partial differential equations using learning. Stabilization is achieved via closure models for ROMs, where we use a model-free extremum seeking (ES) dither-based algorithm to optimally learn the closure models' parameters. We first propose to auto-tune linear closure models using ES, and then extend the results to a closure model combining linear and nonlinear terms, for better stabilization performance. The coupled Burgers' equation is employed as a test-bed for the proposed tuning method.

Source: Proceedings of the ... American Control Conference/Proceedings of the American Control Conference

DOI: 10.1109/acc.2016.7525157

Authors: Mouhacine Benosman, Boris Kramer, Petros T. Boufounos, Piyush Grover

Institutions: Mitsubishi Electric Research Laboratories (United States), Massachusetts Institute of Technology

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2194199387

FWCI: 4.173

Cites: 19

Cited by: 13

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