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SUMMARY:Scalable simulation and inference in non-Gaussian stochastic PDEs 
 - David Duvenaud (University of Toronto)
DTSTART:20221215T110000Z
DTEND:20221215T120000Z
UID:TALK193639@talks.cam.ac.uk
CONTACT:James Allingham
DESCRIPTION:This talk presents early results along a path to scalable appr
 oximate inference schemes in large spatiotemporal models\, such as weather
  or molecular dynamics simulations.  Specifically\, we'll show how existin
 g heuristics for scaling physical models such as coarse grids or mutli-sca
 le temporal models can be learned automatically as auxiliary variables in 
 variational posteriors.  We'll also demonstrate a new contribution to para
 llelizing adaptive SPDE solvers\, allowing stateless sampling of entire Br
 ownian sheets of any dimension. Finally\, we'll show how to extend stochas
 tic variational inference in SDEs to include arbitrary jump processes.
LOCATION:Cambridge University Engineering Department\, CBL Seminar room BE
 4-38.
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