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UID:9db8e9bf5bff5e26e7df60ddaa427f27
CATEGORIES:Applied and Computational Math Seminar
CREATED:20230427T235549
SUMMARY:Machine-Learned Phase Space Discretization for the Topological Analysis of Dynamical Systems
LOCATION:Hill 705
DESCRIPTION:<p><span style="color: #000000; font-family: Arial; font-size: 16px; font-s
 tyle: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-al
 ign: start; text-indent: 0px; text-transform: none; white-space: normal; wi
 dows: 2; word-spacing: 0px; background-color: #ffffff; float: none;">Conley
 's topological approach to dynamics provides a framework for deducing quali
 tative and global information about dynamics that is robust with respect to
  perturbations of the underlying system. Current methods for applications b
 egin with a uniform discretization of the phase space into cubes, followed 
 by problem-specific subdivisions if needed. At fine enough levels of resolu
 tion, the computational cost of these methods can become prohibitively expe
 nsive as dimension increases. We will introduce a machine learning approach
  for discretizing the phase space that aims to overcome the dimension probl
 em. Preliminary experimental results using this method will be discussed.</
 span></p>
CONTACT:Brittany Gelb (Rutgers University)
DTSTAMP:20260826T094726
DTSTART;TZID=America/New_York:20230502T110000
DTEND;TZID=America/New_York:20230502T120000
SEQUENCE:0
TRANSP:OPAQUE
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