BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Data-Driven Reduction of Non-Linearizable Dynamics for Solids\, Fl
 uids and Controls - Proffesor George Haller\, ETH Zurich
DTSTART:20230609T140000Z
DTEND:20230609T150000Z
UID:TALK200194@talks.cam.ac.uk
CONTACT:Jamie Clarkson
DESCRIPTION:Machine learning has been a major development in applied scien
 ce and engineering\, with impressive success stories in static learning en
 vironments like image\, pattern\, and speech recognition. Yet the modeling
  of dynamical phenomena— such as nonlinear vibrations of solids and tran
 sitions in fluids—remains a challenge for classic machine learning. Inde
 ed\, neural net models for nonlinear dynamics tend to be complex\, uninter
 pretable\, and unreliable outside of their training range. In this talk\, 
 I discuss a recent dynamical systems alternative to neural networks in the
  data-driven reduced-order modeling of nonlinear phenomena. Specifically\,
  I show that the concept of spectral submanifolds (SSMs) provides very low
 -dimensional attractors in a large family of mechanics problems ranging fr
 om wing oscillations to transitions in shear flows. A data-driven identifi
 cation of the reduced dynamics on these SSMs gives a rigorous way to const
 ruct accurate and predictive reduced-order models in solid and fluid mecha
 nics without the use of governing equations. I illustrate this on problems
  that include accelerated finite-element simulations of large structures\,
  prediction of transitions in plane Couette flow\, reduced-order modeling 
 of fluid sloshing in a tank\, and model-predictive control of soft robots\
 n\nJoin Zoom Meeting\n \nhttps://eng-cam.zoom.us/j/83729338532 \n \nMeetin
 g ID: 837 2933 8532\n
LOCATION:CivEng Seminar Room (1-33) (Civil Engineering Building)
END:VEVENT
END:VCALENDAR