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UID:20260531T072844EDT-6461U4u9AF@132.216.98.100
DTSTAMP:20260531T112844Z
DESCRIPTION:Centre for Intelligent Machines and REPARTI Seminar\n\nSpeaker:
  Steven Dahdah\, Ph.D student\, McGill University\n\nZoom Link\n	Meeting ID
 : 851 5324 8016\n	Passcode: 007035\n\nAbstract:\n\nUsing the Koopman operat
 or\, nonlinear systems can be expressed as infinite-dimensional linear sys
 tems. Data-driven methods can then be used to approximate a finite-dimensi
 onal Koopman operator\, which is particularly useful for system identifica
 tion\, control\, and state estimation tasks. However\, approximating large
  Koopman operators is numerically challenging\, leading to unstable Koopma
 n operators being identified for otherwise stable systems. Presented are a
  selection of techniques to regularize the Koopman regression problem\, in
 cluding a novel H-infinity norm regularizer. The authors' open-source Koop
 man operator identification library\, pykoop\, is also presented.\n\nBio:
 \n\nSteven Dahdah is a Ph.D. student in the department of Mechanical Engin
 eering at McGill University. He is a member of the DECAR systems group\, w
 hich\, under the guidance of Prof. James Richard Forbes\, conducts researc
 h in the dynamics\, estimation\, and control of aerospace and robotic syst
 ems. He received a B.Eng. in Electrical Engineering from McGill University
  in 2019. His research explores data-driven modelling and control techniqu
 es for industrial robots.\n\nThe talk and slides will be in English.\n
DTSTART:20220224T170000Z
DTEND:20220224T180000Z
LOCATION:CA\, ZOOM
SUMMARY:Data-Driven Modelling and Control with the Koopman Operator
URL:https://www.mcgill.ca/cim/channels/event/data-driven-modelling-and-cont
 rol-koopman-operator-336538
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