Multilevel ensemble Kalman filtering

Handle URI:
http://hdl.handle.net/10754/617315
Title:
Multilevel ensemble Kalman filtering
Authors:
Hoel, Hakon; Law, Kody J. H.; Tempone, Raul ( 0000-0003-1967-4446 )
Abstract:
This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.
KAUST Department:
Applied Mathematics and Computational Science Program
Citation:
Multilevel ensemble Kalman filtering 2016, 54 (3):1813 SIAM Journal on Numerical Analysis
Publisher:
Society for Industrial & Applied Mathematics (SIAM)
Journal:
SIAM Journal on Numerical Analysis
Issue Date:
14-Jun-2016
DOI:
10.1137/15M100955X
Type:
Article
ISSN:
0036-1429; 1095-7170
Sponsors:
This research was supported by King Abdullah University of Science and Technology (KAUST). The authors were members of the SRI Center for Uncertainty Quantification at KAUST for much of the research reported.
Additional Links:
http://epubs.siam.org/doi/10.1137/15M100955X
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorHoel, Hakonen
dc.contributor.authorLaw, Kody J. H.en
dc.contributor.authorTempone, Raulen
dc.date.accessioned2016-07-21T10:56:58Z-
dc.date.available2016-07-21T10:56:58Z-
dc.date.issued2016-06-14-
dc.identifier.citationMultilevel ensemble Kalman filtering 2016, 54 (3):1813 SIAM Journal on Numerical Analysisen
dc.identifier.issn0036-1429-
dc.identifier.issn1095-7170-
dc.identifier.doi10.1137/15M100955X-
dc.identifier.urihttp://hdl.handle.net/10754/617315-
dc.description.abstractThis work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.en
dc.description.sponsorshipThis research was supported by King Abdullah University of Science and Technology (KAUST). The authors were members of the SRI Center for Uncertainty Quantification at KAUST for much of the research reported.en
dc.language.isoenen
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)en
dc.relation.urlhttp://epubs.siam.org/doi/10.1137/15M100955Xen
dc.rightsArchived with thanks to SIAM Journal on Numerical Analysisen
dc.subjectMonte Carloen
dc.subjectmultilevelen
dc.subjectfilteringen
dc.subjectKalman filteren
dc.subjectensemble Kalman filteren
dc.titleMultilevel ensemble Kalman filteringen
dc.typeArticleen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalSIAM Journal on Numerical Analysisen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Mathematics, University of Oslo, Oslo, Norwayen
dc.contributor.institutionComputer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831en
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHoely, HÃ¥konen
kaust.authorTemponex, Raulen
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