The reduced-order hybrid Monte Carlo sampling smoother

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dc.contributor.authorAttia, Ahmeden
dc.contributor.authorStefanescu, Razvanen
dc.contributor.authorSandu, Adrianen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2017-03-06T18:32:47Zen
dc.date.available2017-03-06T18:32:47Zen
dc.date.issued2017-01-10en
dc.description.abstractHybrid Monte-Carlo (HMC) sampling smoother is a fully non-Gaussian four-dimensional data assimilation algorithm that works by directly sampling the posterior distribution formulated in the Bayesian framework. The smoother in its original formulation is computationally expensive due to the intrinsic requirement of running the forward and adjoint models repeatedly. Here we present computationally efficient versions of the HMC sampling smoother based on reduced-order approximations of the underlying model dynamics. The schemes developed herein are tested numerically using the shallow-water equations model on Cartesian coordinates. The results reveal that the reduced-order versions of the smoother are capable of accurately capturing the posterior probability density, while being significantly faster than the original full order formulation.en
dc.description.versionPublished versionen
dc.format.extent28 - 51 (24) page(s)en
dc.identifier.doihttps://doi.org/10.1002/fld.4255en
dc.identifier.issn0271-2091en
dc.identifier.issue1en
dc.identifier.urihttp://hdl.handle.net/10919/75263en
dc.identifier.volume83en
dc.language.isoenen
dc.publisherWiley-Blackwellen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000389330000002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectComputer Science, Interdisciplinary Applicationsen
dc.subjectMathematics, Interdisciplinary Applicationsen
dc.subjectMechanicsen
dc.subjectPhysics, Fluids & Plasmasen
dc.subjectComputer Scienceen
dc.subjectMathematicsen
dc.subjectPhysicsen
dc.subjectdata assimilationen
dc.subjectHamiltonian Monte Carloen
dc.subjectsmoothingen
dc.subjectreduced-order modelingen
dc.subjectproper orthogonal decompositionen
dc.subjectSHALLOW-WATER EQUATIONSen
dc.subjectPROPER ORTHOGONAL DECOMPOSITIONen
dc.subjectVARIATIONAL DATA ASSIMILATIONen
dc.subjectPARTIAL-DIFFERENTIAL-EQUATIONSen
dc.subjectDYNAMIC-MODE DECOMPOSITIONen
dc.subjectNONLINEAR MODELen
dc.subjectEMPIRICAL INTERPOLATIONen
dc.subjectCOHERENT STRUCTURESen
dc.subjectREDUCTIONen
dc.subjectPODen
dc.titleThe reduced-order hybrid Monte Carlo sampling smootheren
dc.title.serialInternational Journal For Numerical Methods in Fluidsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen

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