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dc.contributor.authorHoel, Hakon
dc.contributor.authorChernov, Alexey
dc.contributor.authorLaw, Kody JH
dc.contributor.authorNobile, Fabio
dc.contributor.authorTempone, Raul
dc.date.accessioned2019-01-10T12:30:48Z
dc.date.available2019-01-10T12:30:48Z
dc.date.issued2018-01-10
dc.identifier.urihttp://hdl.handle.net/10754/630788
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 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.)
dc.description.sponsorshipKAUST CRG4 Award Ref:2584
dc.subjectmultilevel Monte Carlo methods, ensemble Kalman filtering, numerical methods for SDE
dc.titleMultilevel ensemble Kalman filtering for spatially extended models
dc.typePresentation
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJan 10th, 2018
dc.conference.nameComputer and Applied Mathematics weekly seminar, Chalmers University of Technology
dc.conference.locationGothenburg, Sweden
dc.contributor.institutionInstitute for Mathematics, Carl Von Ossietsky University, Oldenburg, Germany
dc.contributor.institutionComputer Science and Mathematics division, Oak Ridge National Laboratory
dc.contributor.institutionEPFL
dc.contributor.institutionChalmers University of Technology
refterms.dateFOA2019-01-10T12:30:49Z


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