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SUMMARY:Structural (Aero-) Dynamics Using Gaussian Processes - Igor Kavrak
 ov\, University of Cambridge
DTSTART:20241018T140000Z
DTEND:20241018T150000Z
UID:TALK221320@talks.cam.ac.uk
CONTACT:Sam Cocking
DESCRIPTION:Advances in machine learning and sensors have improved how we 
 model and monitor structures\, yet challenges remain in extracting meaning
 ful information from high-dimensional\, noisy data to create interpretable
  low-order models. This talk presents data-centric methodologies that comb
 ine Gaussian Processes (GPs) as a statistical regression tool with low-ord
 er models for probabilistic inference of structural behaviour. These appro
 aches are designed to address problems in structural (aero-)elasticity\, i
 ncluding modelling nonlinear aeroelastic forces acting on bluff bodies\, i
 nferring the behaviour of elastic beams and plates based on heterogeneous 
 data\, and the inverse reconstruction of dynamic loads. Additionally\, a n
 ovel Bayesian method utilising structured variational inference is introdu
 ced for parameter and state estimation of dynamical systems. Potential app
 lications of this method include reverse engineering low-order dynamical s
 ystems\, such as limit cycle attractors in vortex-induced vibrations. In c
 losing\, and somewhat loosely related to the main topics\, a probabilistic
  structural analysis of a reconstruction of the Lefkandi-Toumba Building\,
  an ancient Greek longhouse from the Greek Dark Ages\, will be presented.\
 n
LOCATION:CivEng Seminar Room (1-33) (Civil Engineering Building)
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