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SUMMARY:Nikolas Nüsken (University of Potsdam)
DTSTART:20200901T120000Z
DTEND:20200901T130000Z
DTSTAMP:20260423T034448Z
UID:DSCSS/9
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/DSCSS/9/">So
 lving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks:
  perspectives from the theory of controlled diffusions and measures on pat
 h space</a>\nby Nikolas Nüsken (University of Potsdam) as part of Data Sc
 ience and Computational Statistics Seminar\n\n\nAbstract\nThe first part o
 f this presentation will review connections between problems in the optima
 l control of diffusion processes\, Hamilton-Jacobi-Bellman equations and f
 orward-backward SDEs\, having in mind applications in rare event simulatio
 n and stochastic filtering. The second part will explain a recent approach
  based on divergences between probability measures on path space and varia
 tional inference that can be used to construct appropriate loss functions 
 in a machine learning framework. This is joint work with Lorenz Richter.\n
LOCATION:https://researchseminars.org/talk/DSCSS/9/
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