Data-Driven Reduced Order Model Stabilization for Partial Differential Equations based on Lyapunov Theory and Extremum Seeking

Friday, February 01, 2019
3:00 pm to 4:00 pm
Floor/Room #: 
202
      

AE 5090. GRADUATE AEROSPACE ENGINEERING COLLOQUIUM


Data-Driven Reduced Order Model Stabilization for Partial Differential Equations based on Lyapunov Theory and Extremum Seeking

Dr. Mouhacine Benosman
Senior Principal Research Scientist
Mitsubishi Electric Research Laboratories
Cambridge, MA 02139-1955 

3:00 PM, Friday, Feb. 1, 2019
Higgins Labs 202
 


Abstract

The problem of reducing a partial differential equation (PDE) to a system of finite dimensional ordinary differential equations (ODE), is of paramount importance in engineering and physics where solving PDE models is often too time consuming. The idea of being able to reduce the PDE model to a simple ODE model without loosing the main characteristics of the original model, such as stability and prediction precision, is appealing for any real-time model-based estimation and control applications. However, this problem remains challenging since model reduction can introduce stability loss and prediction degradation. To remedy these problems many methods have been developed aiming at what is known as stable model reduction.

In this talk, we focus on the so-called closure models and their application in reduced order model (ROM) stabilization. We present some results on robust stabilization for reduced order models (ROM) of partial differential equations using Lyapunov theory. Stabilization is achieved via closure models for ROMs where we use Lyapunov theory to design a new closure model, which is robust with respect to model structured uncertainties. Furthermore, we use an extremum-seeking algorithm to optimally tune the closure models' parameters for optimal ROM stabilization. The 3D Boussinesq equation examples is employed as a test-bed for the proposed stabilization method.

 
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