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Koopman Operator Theory Based Machine Learning of Dynamical Systems

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  • UserIgor Mezic, UC Santa Barbara
  • ClockFriday 10 May 2024, 16:00-17:00
  • HouseMR2.

If you have a question about this talk, please contact Professor Grae Worster.

Many approaches to machine learning have struggled with applications that possess complex process dynamics. In contrast, human intelligence is adapted, and – arguably – built to deal with complex dynamics. The current theory holds that human brain achieves that by constantly rebuilding a model of the world based on the feedback it receives. I will describe an approach to machine learning of dynamical systems based on Koopman Operator Theory (KOT) that also produces generative, predictive, context-aware models amenable to (feedback) control applications. KOT has deep mathematical roots and I will discuss its basic tenets. I will also present computational methods that enable lean computation. A number of examples will be discussed, including use in fluid dynamics, soft robotics, and game dynamics.

This talk is part of the Fluid Mechanics (DAMTP) series.

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