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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20220916T094059Z
DTSTART:20220921T140000Z
DTEND:20220921T150000Z
SUMMARY:Bernhard Schmitzer - Entropic Transfer Operators for Data-Driven 
 Analysis of Dynamical Systems
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}a9i-l84a5i2
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DESCRIPTION:Join us for this research seminar\, part of the SQUIDS (Stati
 stics\, quantification of uncertainty\, inverse problems and data scienc
 e) seminar series.\n\nAbstract: The transfer operator is an elegant way 
 to capture the behaviour of a (stochastic) dynamical system as a linear 
 operator. Spectral analysis can then in principle reveal (almost) invari
 ant measures\, cyclical behaviour\, as well as separation of the dynamic
 s into different time scales. In practice this analysis can rarely be do
 ne analytically\, due to the complexity of the operator or since it may 
 not be known in closed form. A central objective is therefore to numeric
 ally approximate this operator (or its ajoint: the Koopman operator) or 
 to estimate it from data. In this talk we introduce a new estimation met
 hod based on entropic optimal transport and show convergence to a smooth
 ed version of the original operator as more data becomes available. This
  involves an interplay between three different length scales: the discre
 tization scale given by the data\, the blur scale introduced by entropic
  transport\, and the spatial scale of eigenfunctions of the operator.\n\
 nJoint work with Oliver Junge and Daniel Matthes.\nPreprint: http://arxi
 v.org/abs/2204.04901
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:G.108\, Alan Turing Building\, Manchester
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