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UID:20231201T004345-844779804-northwestern.edu
DTSTAMP:20231201T004345
DTSTART:20231023T160000
DTEND:20231023T170000
SUMMARY:Michael Graham: Data, Dynamics, and Manifolds: Machine Learning Approaches for Modeling and Controlling Complex Flows
LOCATION:M113, Technological Institute
DESCRIPTION:Title: Data, Dynamics, and Manifolds: Machine Learning Approaches for Modeling and Controlling Complex Flows\nSpeaker: Michael Graham, Cheimcal & Biological and Mechanical Engineering , University of Wisconsin-Madison\nAbstract: 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.\n**Please note, 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/\n-----\nTo subscribe to the Applied Mathematics Colloquia List send a message to LISTSERV@LISTSERV.IT.NORTHWESTERN.EDU with the command:\nadd esam-seminar@listserv.it.northwestern.edu youremailaddress\n\nPiP URL: https://planitpurple.northwestern.edu/event/604983
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ORGANIZER:McCormick-Engineering Sciences and Applied Mathematics (ESAM)