TR2017-084

Learning-based Robust Stabilization for Reduced-Order Models of 2D and 3D Boussinesq Equations


    •  Benosman, M., Borggaard, J., Kramer, B., "Learning-based Robust Stabilization for Reduced-Order Models of 2D and 3D Boussinesq Equations", Journal of Applied Mathematical Modeling, DOI: 10.1016/j.apm.2017.04.032, Vol. 49, pp. 162-181, September 2017.
      BibTeX TR2017-084 PDF
      • @article{Benosman2017sep,
      • author = {Benosman, Mouhacine and Borggaard, Jeff and Kramer, Boris},
      • title = {Learning-based Robust Stabilization for Reduced-Order Models of 2D and 3D Boussinesq Equations},
      • journal = {Journal of Applied Mathematical Modeling},
      • year = 2017,
      • volume = 49,
      • pages = {162--181},
      • month = sep,
      • publisher = {Elsevier},
      • doi = {10.1016/j.apm.2017.04.032},
      • url = {https://www.merl.com/publications/TR2017-084}
      • }
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  • Research Area:

    Dynamical Systems

We present some results on the stabilization of reduced-order models (ROMs) for thermal fluids. The stabilization is achieved using robust Lyapunov control theory to design a new closure model that is robust to parametric uncertainties. Furthermore, the free parameters in the proposed ROM stabilization method are optimized using a data-driven multi-parametric extremum seeking (MES) algorithm. The 2D and 3D Boussinesq equations provide challenging numerical test cases that are used to demonstrate the advantages of the proposed method.