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dc.contributor.authorHoel, Haakon
dc.contributor.authorChernov, Alexey
dc.contributor.authorLaw, Kody
dc.contributor.authorNobile, Fabio
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-06-08T06:32:30Z
dc.date.available2017-06-08T06:32:30Z
dc.date.issued2016-01-08
dc.identifier.urihttp://hdl.handle.net/10754/624861
dc.description.abstractThe 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 classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.
dc.titleMultilevel ensemble Kalman filtering
dc.typePresentation
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
dc.contributor.institutionUniversity of Oslo
dc.contributor.institutionÉcole Polytechnique Fédérale de Lausanne
dc.contributor.institutionOak Ridge National Laboratory
kaust.personTempone, Raul
refterms.dateFOA2018-06-13T14:46:34Z


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