TR2016-056

Learning-based Reduced Order Model Stabilization for Partial Differential Equations: Application to the Coupled Burgers' Equation


    •  Benosman, M.; Boufounos, P.T.; Grover, P.; Kramer, B., "Learning-based Reduced Order Model Stabilization for Partial Differential Equations: Application to the Coupled Burgers' Equation", American Control Conference (ACC), July 2016.
      BibTeX Download PDF
      • @inproceedings{Benosman2016jul2,
      • author = {Benosman, M. and Boufounos, P.T. and Grover, P. and Kramer, B.},
      • title = {Learning-based Reduced Order Model Stabilization for Partial Differential Equations: Application to the Coupled Burgers' Equation},
      • booktitle = {American Control Conference (ACC)},
      • year = 2016,
      • month = jul,
      • url = {http://www.merl.com/publications/TR2016-056}
      • }
  • MERL Contacts:
  • Research Area:

    Multimedia


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.