TR2016-056
Learning-based Reduced Order Model Stabilization for Partial Differential Equations: Application to the Coupled Burgers' Equation
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- "Learning-based Reduced Order Model Stabilization for Partial Differential Equations: Application to the Coupled Burgers' Equation", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525157, July 2016, pp. 1673-1678.BibTeX TR2016-056 PDF
- @inproceedings{Benosman2016jul2,
- author = {Benosman, Mouhacine and Boufounos, Petros T. and Grover, Piyush and Kramer, Boris},
- title = {Learning-based Reduced Order Model Stabilization for Partial Differential Equations: Application to the Coupled Burgers' Equation},
- booktitle = {American Control Conference (ACC)},
- year = 2016,
- pages = {1673--1678},
- month = jul,
- doi = {10.1109/ACC.2016.7525157},
- url = {https://www.merl.com/publications/TR2016-056}
- }
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- "Learning-based Reduced Order Model Stabilization for Partial Differential Equations: Application to the Coupled Burgers' Equation", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525157, July 2016, pp. 1673-1678.
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Research Areas:
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 modelfree 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.
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NEWS MERL makes a strong showing at the American Control Conference Date: July 6, 2016 - July 8, 2016
Where: American Control Conference (ACC)
MERL Contacts: Scott A. Bortoff; Petros T. Boufounos; Stefano Di Cairano; Abraham Goldsmith; Christopher R. Laughman; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Machine LearningBrief- The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.