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X-WR-CALDESC:FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship
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DTSTART;TZID=Europe/Berlin:20231019T170000
DTEND;TZID=Europe/Berlin:20231019T180000
DTSTAMP:20231003T163528Z
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SUMMARY:Data-driven system analysis: Polynomial optimization meets Koopman
DESCRIPTION:Next Thursday October 19, 2023 our Senior scientist Prof. Dr. Giovanni Fantuzzi will talk on “Data-driven system analysis: Polynomial optimization meets Koopman” at the Aerodynamics & Control Seminars, hosted by the Department of Aeronautics at Imperial College London. \nAbstract. Questions about stability, long-time behaviour, and the effect of uncertainty are at the heart of nonlinear system analysis. Many of these questions can be answered using “auxiliary functions”, which generalize the Lyapunov functions encountered in stability analysis. Moreover, auxiliary functions can be constructed using polynomial optimization if a system is governed by known polynomial equations. But what if the governing equations are not polynomial or, worse, are not known? In this talk, I will show that auxiliary functions can be “discovered” directly from data if one combines polynomial optimization with (extended) dynamic mode decomposition. This enables one to perform data-driven system analysis without having to first identify a model for the system dynamics. The key to this is a previously unrecognized connection between auxiliary functions and the Koopman operator, which can be approximated from data with rigorous convergence guarantees. After explaining this connection, I will present examples illustrating how the method allows one to perform stability analysis, bound long-time average behaviour, and extract unstable periodic orbits for low-dimensional chaotic attractors.\nAll results in this talk were obtained in collaboration with Jason Bramburger (Concordia University).\nReferences\n1. Jason J. Bramburger and Giovanni Fantuzzi (2023) Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization.\n2. Jason J. Bramburger and Giovanni Fantuzzi (2023) Data-driven Discovery of Invariant Measures. \nWHEN\nThu. October 19, 2023 at 17:00H\nWHERE\nOn-site / Online\nOn-site: CAGB 320-321\nOnline: https://cassyni.com/events/BaNTrWtYX3woCAoRx73bZf\n
URL:https://dcn.nat.fau.eu/events/data-driven-system-analysis-polynomial-optimization-meets-koopman/
CATEGORIES:Seminar/Talk,Workshop
ATTACH;FMTTYPE=image/png:https://dcn.nat.fau.eu/wp-content/uploads/ICLondon_gFantuzzi_19oct2023.png
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