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UID:20251122T195648EST-6348Sa9ePn@132.216.98.100
DTSTAMP:20251123T005648Z
DESCRIPTION:Title: Particle Filters and Finite Ensemble Kalman Filters in L
 arge Dimensions: Theory\, Applied Practice\, and New Phenomena\n\nAbstract
 :\n\nThe filtering and predictive skill for turbulent signals is often lim
 ited by the lack of information about the true dynamics of the system and 
 by our inability to resolve the assumed dynamics with sufficiently high re
 solution using the current computing power. The classical Kalman filter is
  no longer computationally feasible in such a high dimensional context. Th
 is problem can often be resolved by exploiting the underlying multiscale s
 tructure\, applying the full Kalman filtering procedures only to the large
  scale variables\, and estimating the small scale variables with proper st
 atistical strategies\, including multiplicative inflation\, representation
  model error in the observations\, and crude localization. A new error ana
 lysis framework for different reduced random Kalman filters is established
 \, The classical tools for Kalman filters can be used as a-priori performa
 nce criteria for the reduced filters. In applications\, these criteria gua
 rantee the reduced filters are robust\, and accurate for small noise syste
 ms. They also shed light on how to tune the reduced filters for stochastic
  turbulence. A new class of particle filters\, clustered particle filters\
 , is also introduced for high-dimensional nonlinear systems. The clustered
  particle filter captures non-Gaussian features of the true signal which a
 re typical in complex nonlinear dynamical systems such as geophysical syst
 ems.\n
DTSTART:20190516T211500Z
DTEND:20190516T221500Z
LOCATION:Room 1140\, CA\, Pav. André-Aisenstadt
SUMMARY:Distinguished Lecture: Andrew J. Majda (Courant Institute\, NYU)
URL:/mathstat/channels/event/distinguished-lecture-and
 rew-j-majda-courant-institute-nyu-297054
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