Least squares approach for initial data recovery in dynamic data-driven applications simulations

Handle URI:
http://hdl.handle.net/10754/598715
Title:
Least squares approach for initial data recovery in dynamic data-driven applications simulations
Authors:
Douglas, C.; Efendiev, Y.; Ewing, R.; Ginting, V.; Lazarov, R.; Cole, M.; Jones, G.
Abstract:
In 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.
Citation:
Douglas 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.
Publisher:
Springer Nature
Journal:
Computing and Visualization in Science
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Dec-2010
DOI:
10.1007/s00791-011-0154-8
Type:
Article
ISSN:
1432-9360; 1433-0369
Sponsors:
Research 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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorDouglas, C.en
dc.contributor.authorEfendiev, Y.en
dc.contributor.authorEwing, R.en
dc.contributor.authorGinting, V.en
dc.contributor.authorLazarov, R.en
dc.contributor.authorCole, M.en
dc.contributor.authorJones, G.en
dc.date.accessioned2016-02-25T13:34:58Zen
dc.date.available2016-02-25T13:34:58Zen
dc.date.issued2010-12en
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.en
dc.identifier.issn1432-9360en
dc.identifier.issn1433-0369en
dc.identifier.doi10.1007/s00791-011-0154-8en
dc.identifier.urihttp://hdl.handle.net/10754/598715en
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.en
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).en
dc.publisherSpringer Natureen
dc.subjectDynamic data-driven applications simulations (DDDAS)en
dc.subjectInitial data recoveryen
dc.subjectLeast squaresen
dc.subjectParameters updateen
dc.titleLeast squares approach for initial data recovery in dynamic data-driven applications simulationsen
dc.typeArticleen
dc.identifier.journalComputing and Visualization in Scienceen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionUniversity of Utah, Salt Lake City, United Statesen
dc.contributor.institutionUniversity of Wyoming, Laramie, United Statesen
dc.contributor.institutionYale University, New Haven, United Statesen
kaust.grant.numberKUS-C1-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.