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DESCRIPTION:Data-driven system analysis using polynomial optimization and t
 he Koopman operator\n\nJason Bramburger\, Concordia University\n	Tuesday Se
 ptember 10\, 12-1pm\n	Zoom Link: https://mcgill.zoom.us/j/89914150820\n	In P
 erson: 550 Sherbrooke\, Room 189\n	\n	Abstract: Many important statements ab
 out dynamical systems can be proven by finding scalar-valued auxiliary fun
 ctions whose time evolution along trajectories obeys certain pointwise ine
 quality that imply the desired result. The most familiar of these auxiliar
 y functions is a Lyapunov function to prove steady-state stability\, but s
 uch functions can also be used to bound averages of ergodic systems\, defi
 ne trapping boundaries\, and so much more. In this talk I will highlight a
  method of identifying auxiliary functions from data using polynomial opti
 mization. The method leverages recent advances in approximating the Koopma
 n operator from data\, so-called extended dynamic mode decomposition\, to 
 provide system-level information without system identification. The result
  is a flexible\, data-driven\, model-agnostic computational method that do
 es not require explicit model discovery. Furthermore\, it can be applied t
 o data generated through deterministic or stochastic processes with no pri
 or adjustments to the implementation. It can be used to bound quantities o
 f interest\, develop optimal state-dependent feedback controllers\, and di
 scover invariant measures.\n
DTSTART:20240910T160000Z
DTEND:20240910T170000Z
SUMMARY:QLS Seminar Series - Jason Bramburger
URL:/qls/channels/event/qls-seminar-series-jason-bramb
 urger-358516
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