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UID:20260916T030435EDT-5670FWvNjb@132.216.98.100
DTSTAMP:20260916T070435Z
DESCRIPTION:\n	\n		\n			\n				APPLIED MATH SEMINAR\n\n				TITLE / TITRE\n					Identifying and 
 suppressing unknown disturbances to dynamical systems using machine learni
 ng\n					\n					ABSTRACT /RÉSUMÉ\n\n				Recent years have seen an explosion in the use o
 f machine learning techniques for studying nonlinear dynamical systems. He
 re we explore how machine learning can be used to identify and subsequentl
 y suppress unknown disturbances to complex systems. We find that unknown d
 isturbances can be accurately detected with reservoir computer architectur
 es even when no knowledge of the underlying dynamics is assumed. All that 
 is required are very mild conditions on the forcing functions used to trai
 n the reservoir. Moreover\, we also show that this framework can be extend
 ed to suppress the disturbances\, i.e.\, controlling the system to recover
  the undisturbed dynamics. We illustrate our method with the identificatio
 n of unknown disturbances to an analog electric chaotic circuit as well as
  numerical simulations of isolated and network-coupled nonlinear systems.
 \n\n				 \n\n				 \n			\n		\n	\n\n
DTSTART:20231120T210000Z
DTEND:20231120T220000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Per Sebastian Skardal (Trinity College)
URL:https://www.mcgill.ca/mathstat/channels/event/sebastian-skardal-trinity
 -college-352518
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