Show simple item record

dc.contributor.authorDouglas, C.
dc.contributor.authorEfendiev, Y.
dc.contributor.authorEwing, R.
dc.contributor.authorGinting, V.
dc.contributor.authorLazarov, R.
dc.contributor.authorCole, M.
dc.contributor.authorJones, G.
dc.date.accessioned2016-02-25T13:34:58Z
dc.date.available2016-02-25T13:34:58Z
dc.date.issued2011-05-19
dc.identifier.citationDouglas C, Efendiev Y, Ewing R, Ginting V, Lazarov R, et al. (2010) Least squares approach for initial data recovery in dynamic data-driven applications simulations. Computing and Visualization in Science 13: 365–375. Available: http://dx.doi.org/10.1007/s00791-011-0154-8.
dc.identifier.issn1432-9360
dc.identifier.issn1433-0369
dc.identifier.doi10.1007/s00791-011-0154-8
dc.identifier.urihttp://hdl.handle.net/10754/598715
dc.description.abstractIn this paper, we consider the initial data recovery and the solution update based on the local measured data that are acquired during simulations. Each time new data is obtained, the initial condition, which is a representation of the solution at a previous time step, is updated. The update is performed using the least squares approach. The objective function is set up based on both a measurement error as well as a penalization term that depends on the prior knowledge about the solution at previous time steps (or initial data). Various numerical examples are considered, where the penalization term is varied during the simulations. Numerical examples demonstrate that the predictions are more accurate if the initial data are updated during the simulations. © Springer-Verlag 2011.
dc.description.sponsorshipResearch of the authors is partially supported by NSF grantITR-0540136 and by award KUS-C1-016-04, made by King AbdullahUniversity of Science and Technology (KAUST).
dc.publisherSpringer Nature
dc.subjectDynamic data-driven applications simulations (DDDAS)
dc.subjectInitial data recovery
dc.subjectLeast squares
dc.subjectParameters update
dc.titleLeast squares approach for initial data recovery in dynamic data-driven applications simulations
dc.typeArticle
dc.identifier.journalComputing and Visualization in Science
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionUniversity of Utah, Salt Lake City, United States
dc.contributor.institutionUniversity of Wyoming, Laramie, United States
dc.contributor.institutionYale University, New Haven, United States
kaust.grant.numberKUS-C1-016-04
dc.date.published-online2011-05-19
dc.date.published-print2010-12


This item appears in the following Collection(s)

Show simple item record