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CMX Lunch Seminar

Wednesday, October 20, 2021
12:00pm to 1:00pm
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Online Event
Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
Steven Brunton, James B. Morrison Endowed Career Development Professor in Mechanical Engineering, Mechanical Engineering, University of Washington,

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 in fluid dynamics, and we will discuss how to incorporate these models into existing model-based control efforts. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.

For more information, please contact Jolene Brink by phone at (626)395-2813 or by email at jbrink@caltech.edu or visit CMX Website.