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dc.contributor.authorHoel, Håkon
dc.contributor.authorShaimerdenova, Gaukhar
dc.contributor.authorTempone, Raul
dc.date.accessioned2021-01-27T13:15:21Z
dc.date.available2020-02-04T11:18:30Z
dc.date.available2021-01-27T13:15:21Z
dc.date.issued2020
dc.identifier.citationHoel, H., Shaimerdenova, G., & Tempone, R. (2020). Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators. Foundations of Data Science, 2(4), 351–390. doi:10.3934/fods.2020017
dc.identifier.issn2639-8001
dc.identifier.doi10.3934/fods.2020017
dc.identifier.urihttp://hdl.handle.net/10754/661369
dc.description.abstractWe introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF method is fundamentally different from the preexisting method introduced by Hoel, Law and Tempone in 2016, and it is suitable for extensions towards multi-index Monte Carlo based filtering methods. Robust theoretical analysis and supporting numerical examples show that under appropriate regularity assumptions, the MLEnKF method has better complexity than plain vanilla EnKF in the large-ensemble and fine-resolution limits, for weak approximations of quantities of interest. The method is developed for discrete-time filtering problems with finite-dimensional state space and linear observations polluted by additive Gaussian noise.
dc.description.sponsorshipThis work was funded by the KAUST Office of Sponsored Research (OSR) under Award No. URF/1/2584-01-01; the Alexander von Humboldt Foundation; the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the German State of North Rhine-Westphalia (MKW) under the Excellence Strategy of the Federal Government and the Länder. G.Shaimerdenova and R. Tempone are members of the KAUST SRI Center for Uncertainty Quantification in Computational Science and Engineering.
dc.publisherAmerican Institute of Mathematical Sciences (AIMS)
dc.relation.urlhttp://aimsciences.org//article/doi/10.3934/fods.2020017
dc.rightsThis is a pre-copy-editing, author-produced PDF of an article accepted for publication in Foundations of Data Science following peer review. The definitive publisher-authenticated version is available online at: http://doi.org/10.3934/fods.2020017
dc.subjectMonte Carlo, multilevel, convergence rates, Kalman filter, ensemble Kalman filter.
dc.titleMultilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalFoundations of Data Science
dc.rights.embargodate2022-01-27
dc.eprint.versionPost-print
dc.contributor.institutionChair of Mathematics for Uncertainty Quantification, RWTH Aachen University, Aachen, Germany
dc.identifier.volume2
dc.identifier.issue4
dc.identifier.pages351-390
dc.identifier.arxivid2002.00480
kaust.personShaimerdenova, Gaukhar
kaust.personTempone, Raul
kaust.grant.numberURF/1/2584-01-01
dc.relation.issupplementedbyhttps://github.com/GaukharSH/mlenkf
dc.relation.issupplementedbygithub:GaukharSH/mlenkf
refterms.dateFOA2020-02-04T00:00:00Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: GaukharSH/mlenkf: Julia Implementation of Multilevel Ensemble Kalman Filtering (MLEnKF) with Local Kalman Gains. Publication Date: 2019-12-22. github: <a href="https://github.com/GaukharSH/mlenkf" >GaukharSH/mlenkf</a> Handle: <a href="http://hdl.handle.net/10754/667585" >10754/667585</a></a></li></ul>
kaust.acknowledged.supportUnitKAUST Office of Sponsored Research (OSR)
kaust.acknowledged.supportUnitSRI Center for Uncertainty Quantification in Computational Science and Engineering.


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