For our next “Control meets Learning” virtual seminar, we are excited to have Prof. Steven L. Brunton from the University of Washington, Seattle. The talk will start at 9:00 AM Pacific Time on Wednesday, May 5, 2021. To join the seminar, you may use the Zoom link below or watch the livestream on YouTube.
Zoom:
https://caltech.zoom.us/j/83803287206YouTube:
https://www.youtube.com/watch?v=4P2slJAVslQTitle: Data-Driven Dynamical Systems and Control
Abstract: Accurate and efficient reduced-order models are essential to understand, predict, estimate, and control complex, multiscale and nonlinear dynamical systems. Machine learning constitutes a growing set of powerful techniques to extract patterns and build models from this data, complementing the existing theoretical, numerical and experimental efforts. These models should ideally be generalizable, interpretable and based on limited training data. In this talk, I will discuss several modern perspectives on data-driven control of nonlinear systems, including the dynamic mode decomposition (DMD), Koopman operator theory, and the sparse identification of nonlinear dynamics (SINDy) approach. SINDy in particular provides a general framework to discover the governing equations underlying a dynamical system simply from measurement data, 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.
Bio: Dr. 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. Steve received the B.S. in mathematics from Caltech in 2006 and the Ph.D. 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, the Presidential Early Career Award for Scientists and Engineers (PECASE), and he was awarded the University of Washington College of Engineering junior faculty and teaching awards.
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