Title: Data, Dynamics, and Manifolds: Machine Learning Approaches for Modeling and Controlling Complex Flows

Speaker: Michael Graham, Chemical Biological and Mechanical Engineering, University of Wisconsin-Madison

Abstract: This lecture will outline how exploitation of symmetries substantially improves performance in accurately simulating chaotic or turbulent fluid flows. Graham will describe a data-driven reduced order modeling method, "Data-driven Manifold Dynamics," that finds a nonlinear coordinate representation of the manifold using a machine-learning architecture called an autoencoder, then learns an ordinary differential equation for the dynamics on the manifold.

Special Note: This is the 2023 Stephen H. Davis Lecture. Note the unusual day, time, and location.

Zoom: This event will be held in-person, as well as online via Zoom. Register for attendance or for the webinar at the following link: https://www.mccormick.northwestern.edu/applied-math/news-events/stephen-davis-symposium/

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