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UID:1187170725f986e93aa5a330cea1942e
CATEGORIES:Mathematical Physics Seminar
CREATED:20231101T102134
SUMMARY:Konstantin Mischaikow - Identifying Nonlinear Dynamics from Sparse Data
LOCATION:Hill 705
DESCRIPTION:<p style="margin-bottom: 6px; text-align: center; background: #f7f7f7;"><st
 rong>Konstantin Mischaikow&nbsp;– Rutgers University</strong></p><p style="
 margin-bottom: 6px; text-align: center; background: #f7f7f7;"><strong>Date/
 Time/Location<br> Thursday, </strong><strong>November 9th</strong><strong>,
  12:00pm; Hill Center 705</strong></p><p style="text-align: center; backgro
 und: #f7f7f7;">&nbsp;<strong>&nbsp;</strong><strong>Identifying Nonlinear D
 ynamics from Sparse Data</strong></p><p><strong>There are a variety of stat
 istical techniques that given sufficient time series&nbsp; identify explici
 t models, e.g. differential equations or maps, that are then evaluated to p
 redict dynamics. However, it is well established that bifurcations can take
  place on all scales and hence dynamics is sensitive&nbsp; to the choice of
  model and hence to small errors in data. This suggests a potential inheren
 t instability in going directly from data to models. We propose a novel met
 hod, combining Conley theory and Gaussian Process surrogate modeling with u
 ncertainty quantification, through which it is possible to characterize loc
 al and global dynamics, e.g., existence of fixed points, periodic orbits, c
 onnecting orbits, bistability, and chaotic dynamics, with lower bounds on t
 he confidence that this characterization of the dynamics is co</strong></p>
DTSTAMP:20260831T083557
DTSTART;TZID=America/New_York:20231109T120000
DTEND;TZID=America/New_York:20231109T130000
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