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BEGIN:VEVENT
DTSTAMP:20260625T224813Z
DTSTART;VALUE=DATE-TIME:20100521T130000
DTEND;VALUE=DATE-TIME:20100521T140000
SUMMARY:Complexity Forum - Misha Chertkov
TZID:Europe/London
UID:20100521-094d43d5385c299a01386c3bd8b66ec6@warwick.ac.uk
CREATED:20100426T090802Z
DESCRIPTION:Speaker: Misha Chertkov (LANL) Title: Tracking particles by p
 assing messages between images Abstract: We report construction of an ef
 ficient algorithm for learning parameters of dynamical statistical model
 s\, mapping many identical particles/markers from one image to another\,
  given that positions of all the particles in both images are known. Our
  algorithm belongs to the class of the message-passing algorithms\, also
  known in Computer Science\, Information Theory and Statistical Physics 
 under the name of Belief Propagation (BP). The algorithm is distributed 
 thus allowing parallel implementation suitable for computations on multi
 ple machines without significant inter-machine computational overhead. W
 e test our algorithm on a model example of reconstructing diffusion coef
 ficient and velocity gradient tensor from two consecutive images of many
  particles seed in two-dimensional and three-dimensional flows. Our nume
 rical experiments show that this BP algorithm compares in quality of the
  parameter prediction with exact Markov Chain Monte-Carlo algorithm\, wh
 ile BP is far more superior in speed. We also suggest and analyze a rand
 om-distance model providing theoretical justification for BP accuracy in
  the diffusion and velocity gradient reconstruction. Our main conclusion
  is that the developed technique offers a very significant ``software" i
 mprovement in speed for Particle Image Velocimetry (PIV) technique\, whi
 ch currently constitutes the main working horse in experimental turbulen
 t research. The talk is based on PNAS 10.1073/pnas.0910994107 published 
 in Apr\, 2010.
LOCATION:
CATEGORIES:Seminar Room D1.07,forum
LAST-MODIFIED:20120709T145408Z
ORGANIZER;CN=Colm Connaughton:
END:VEVENT
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