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    Multilevel ensemble Kalman filtering for spatially extended models

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    Type
    Presentation
    Authors
    Hoel, Hakon
    Chernov, Alexey
    Law, Kody JH
    Nobile, Fabio
    Tempone, Raul cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-01-10
    Permanent link to this record
    http://hdl.handle.net/10754/630788
    
    Metadata
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    Abstract
    The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of the classical Monte Carlo method, which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this talk I will present ideas on combining MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite and infinite dimensional state spaces. Theoretical results and numerical studies of the performance gain of MLEnKF over EnKF will also be presented. (Joint work with Alexey Chernov, Kody J. H. Law, Fabio Nobile, and Raul Tempone.)
    Sponsors
    KAUST CRG4 Award Ref:2584
    Conference/Event name
    Computer and Applied Mathematics weekly seminar, Chalmers University of Technology
    Collections
    Presentations; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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