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UID:www.tcs.tifr.res.in/event/917
DTSTAMP:20230914T125943Z
SUMMARY:Data Assimilation for Deterministic High Dimensional Dynamical Syst
 ems
DESCRIPTION:Speaker: Amit Apte (International Centre for Theoretical Scienc
 es\nTata Institute of Fundamental Research\nBengaluru North 560089 )\n\nA
 bstract: \nData assimilation refers to the problem of estimation of state 
 of a high dimensional chaotic system given noisy\, partial observations of
  the system. A variety of techniques\, mainly falling in one of two broad 
 classes of either variational or Bayesian\, have been developed in the con
 text of earth sciences\, mainly for weather prediction purposes. This talk
  will focus on the Bayesian viewpoint (nonlinear filtering for determinist
 ic dynamics)\, illustrating how the characteristics of the dynamics of the
  system play a crucial role in determining the properties of filtering dis
 tribution. One example of this relation is our recent work (doi:10.1137/15
 M1025839\, doi:10.1137/16M1068712) related to the convergence of the Kalma
 n filter covariance matrix onto the unstable-neutral subspace for a linear
 \, deterministic dynamical system with linear observation operator\, which
  I will discuss briefly. The second example will focus on Lagrangian data 
 assimilation (LaDA) which refers to the use of observations provided by (p
 seudo-)Lagrangian instruments such as drifters\, floats\, and gliders\, wh
 ich are important sources of surface and subsurface data for the oceans. I
  will describe our recent proposal (doi:10.1175/MWR-D-14-00051.1\, doi.org
 /10.1007/978-3-319-25138-7_24) for a hybrid particle-Kalman filter method 
 for LaDA\, which combines the strengths of both these filters and the spec
 ific dynamical structure of the Lagrangian dynamics.\n
URL:https://www.tcs.tifr.res.in/web/events/917
DTSTART;TZID=Asia/Kolkata:20181120T140000
DTEND;TZID=Asia/Kolkata:20181120T150000
LOCATION:A-201 (STCS Seminar Room)
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