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SUMMARY:Adaptive probabilistic ODE solvers without adaptive memory require
 ments - Nico Krämer - Danish Technical University
DTSTART:20241107T160000Z
DTEND:20241107T170000Z
UID:TALK222397@talks.cam.ac.uk
CONTACT:Carl Henrik Ek
DESCRIPTION:Adaptive probabilistic solvers for ordinary differential equat
 ions (ODEs) have made substantial progress in recent years but can still n
 ot solve memory-demanding differential equations. In this talk\, I review 
 recent developments in numerically robust fixed-point smoothers and how to
  use them for constructing adaptive probabilistic ODE solvers. These new a
 lgorithms use drastically less memory than their predecessors and are the 
 first adaptive probabilistic numerical methods compatible with scientific 
 computing in JAX.\n
LOCATION:SS03\, Computer Lab
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