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CATEGORIES:Fluid Mechanics (DAMTP)
SUMMARY:Data-driven modeling and control via spectral subm
 anifolds - George Haller\, ETH Zurich
DTSTART;TZID=Europe/London:20260313T160000
DTEND;TZID=Europe/London:20260313T170000
UID:TALK243409AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/243409
DESCRIPTION:I discuss a dynamical systems alternative to neura
 l networks in the data-driven reduced-order modeli
 ng of nonlinear phenomena. Specifically\, the rece
 nt concept of spectral submanifolds (SSMs) provide
 s very low-dimensional attractors in virtually all
  dynamics problems of physical importance. A data-
 driven identification of the reduced dynamics on t
 hese SSMs gives a mathematically justified way to 
 construct accurate and predictive reduced-order mo
 dels for solids\, fluids\,  and controls without t
 he use of governing equations. I illustrate this o
 n physical problems including structural vibration
 s\, fluid-structure interactions\, shear flows\, p
 lant dynamics\, and the model-predictive control o
 f soft robots.
LOCATION:MR2
CONTACT:Duncan Hewitt
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