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dc.contributor.authorElsheikh, Ahmed H.
dc.contributor.authorTavakoli, Reza
dc.contributor.authorWheeler, Mary Fanett
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-08-03T11:05:36Z
dc.date.available2015-08-03T11:05:36Z
dc.date.issued2013-06
dc.identifier.issn00457825
dc.identifier.doi10.1016/j.cma.2013.02.012
dc.identifier.urihttp://hdl.handle.net/10754/562784
dc.description.abstractA novel parameter estimation algorithm is proposed. The inverse problem is formulated as a sequential data integration problem in which Gaussian process regression (GPR) is used to integrate the prior knowledge (static data). The search space is further parameterized using Karhunen-Loève expansion to build a set of basis functions that spans the search space. Optimal weights of the reduced basis functions are estimated by an iterative stochastic ensemble method (ISEM). ISEM employs directional derivatives within a Gauss-Newton iteration for efficient gradient estimation. The resulting update equation relies on the inverse of the output covariance matrix which is rank deficient.In the proposed algorithm we use an iterative regularization based on the ℓ2 Boosting algorithm. ℓ2 Boosting iteratively fits the residual and the amount of regularization is controlled by the number of iterations. A termination criteria based on Akaike information criterion (AIC) is utilized. This regularization method is very attractive in terms of performance and simplicity of implementation. The proposed algorithm combining ISEM and ℓ2 Boosting is evaluated on several nonlinear subsurface flow parameter estimation problems. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates. © 2013 Elsevier B.V.
dc.publisherElsevier BV
dc.subjectBoosting
dc.subjectGaussian process regression
dc.subjectIterative stochastic ensemble method
dc.subjectKarhunen-Loève expansion
dc.subjectParameter estimation
dc.subjectSubsurface flow models
dc.titleBoosting iterative stochastic ensemble method for nonlinear calibration of subsurface flow models
dc.typeArticle
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.identifier.journalComputer Methods in Applied Mechanics and Engineering
dc.contributor.institutionCenter for Subsurface Modeling (CSM), Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX, United States
kaust.personHoteit, Ibrahim
kaust.personElsheikh, Ahmed H.


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