About the talk
In this talk we first consider the situation where one is interested in gaining understanding of general dynamical properties of a chaotically time evolving system solely through access to time series measurements that depend on the evolving state of an, otherwise unknown, system. Using examples, we show that machine learning is an extremely effective tool for accomplishing this task, and we discuss how the ability to do this can be of practical utility.
In the second part of the talk, we turn the problem around and utilize chaos theory to explain the dynamical basis for how a machine learning system is able to do accomplish this task [Z. Lu, B. Hunt and E. Ott, CHAOS (2018)].
About the speaker
Edward Ott received his Ph.D. from The Polytechnic Institute of Brooklyn and was an NSF Postdoctoral Fellow at Cambridge University, following which he became a faculty member of the Department of Electrical Engineering at Cornell University. After 11 years at Cornell, he moved to the University of Maryland where he is currently a Distinguished University Professor in the Department of Physics and the Department of Electrical and Computer Engineering. Prof. Ott is best known for his research in basic and applied aspects of chaotic dynamics for which he has received several notable honors, e.g., the Julius Edgar Lilienfeld Prize from the American Physical Society ( “For pioneering contributions in nonlinear dynamics and chaos theory….”) He is an author of over 400 published scientific papers, as well as the book, “Chaos in Dynamical Systems.”
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