[Seminar] Machine Learning and Sparse Modeling for Scientific Discovery, with Examples in Fluid Mechanics by Prof. Steven Brunton

Title: 

 Machine Learning and Sparse Modeling for Scientific Discovery, with Examples in Fluid Mechanics

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 describes how machine learning may be used to develop accurate and efficient nonlinear dynamical systems models for
complex natural and engineered systems. We explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which
identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach
tends to promote models that are interpretable and generalizable, capturing the essential “physics” of the system. We also discuss
the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling
approach will be demonstrated on a range of challenging modeling problems, for example in fluid dynamics, and we will discuss how
to incorporate these models into existing model-based control efforts.

Bio:

Dr. Steven L. Brunton is a Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Professor of
Applied Mathematics, Aeronautics and astronautics, and Computer science, and he is also a Data Science Fellow at the eScience
Institute. He is Director of the AI Center for Dynamics and Control (ACDC) at UW and is Associate Director for the NSF AI Institute in
Dynamic Systems. 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 received the Army and Air Force Young Investigator
Program (YIP) awards and the Presidential Early Career Award for Scientists and Engineers (PECASE). Steve is also passionate about
teaching math to engineers as co-author of four textbooks and through his popular YouTube channel, under the moniker
“eigensteve”.

Zoom:

URL: https://oist.zoom.us/j/97407916780?pwd=rSkvpq51ZYtR6ro6EajPO30slye3jo.1&from=addon
Meeting ID: 974 0791 6780
Passcode:  379677