Seminar: Data-Driven Discovery and Control of Complex Systems

Steve Brunton, PhD, University of Washington

All dates for this event occur in the past.

Scott Laboratory
Scott Laboratory
Room E525
201 W 19th Ave
Columbus, OH 43210
United States

Abstract

 

Accurate and efficient reduced-order models are essential to understand, predict, estimate and control complex, multiscale and nonlinear dynamical systems. These models should ideally be generalizable, interpretable and based on limited training data. This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time, closed-loop feedback control.  First, the presenter will discuss how it is possible to enforce known constraints, such as energy conserving quadratic nonlinearities in incompressible fluids, to “bake in” known physics.  Next, it will be demonstrated that higher-order nonlinearities can approximate the effect of truncated modes, resulting in more accurate models of lower order than Galerkin projection. Finally, the presenter will discuss how to design sparse sensors and design models based on intrinsic measurements of the system.  

 

About the speaker

Steven L. Brunton is an associate professor of mechanical engineering at the University of Washington. He is also adjunct associate professor of applied mathematics and a data science Fellow at the eScience Institute. Brunton received a BS in mathematics from Caltech in 2006 and a PhD in mechanical and aerospace engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems and manufacturing. He is a co-author of three textbooks, received the Army and Air Force Young Investigator Program awards and Army Early Career Award for Scientists and Engineers (ECASE), and he was awarded the University of Washington College of Engineering junior faculty and teaching awards.

 

Hosted by Prof. Jack McNamara.