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dc.contributor.authorElsheikh, Ahmed H.
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorWheeler, Mary Fanett
dc.date.accessioned2015-08-03T11:46:49Z
dc.date.available2015-08-03T11:46:49Z
dc.date.issued2014-02
dc.identifier.citationElsheikh, A. H., Hoteit, I., & Wheeler, M. F. (2014). Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates. Computer Methods in Applied Mechanics and Engineering, 269, 515–537. doi:10.1016/j.cma.2013.11.001
dc.identifier.issn00457825
dc.identifier.doi10.1016/j.cma.2013.11.001
dc.identifier.urihttp://hdl.handle.net/10754/563367
dc.description.abstractAn efficient Bayesian calibration method based on the nested sampling (NS) algorithm and non-intrusive polynomial chaos method is presented. Nested sampling is a Bayesian sampling algorithm that builds a discrete representation of the posterior distributions by iteratively re-focusing a set of samples to high likelihood regions. NS allows representing the posterior probability density function (PDF) with a smaller number of samples and reduces the curse of dimensionality effects. The main difficulty of the NS algorithm is in the constrained sampling step which is commonly performed using a random walk Markov Chain Monte-Carlo (MCMC) algorithm. In this work, we perform a two-stage sampling using a polynomial chaos response surface to filter out rejected samples in the Markov Chain Monte-Carlo method. The combined use of nested sampling and the two-stage MCMC based on approximate response surfaces provides significant computational gains in terms of the number of simulation runs. The proposed algorithm is applied for calibration and model selection of subsurface flow models. © 2013.
dc.publisherElsevier BV
dc.subjectLeast Angle Regression
dc.subjectNested sampling
dc.subjectPolynomial chaos expansion
dc.subjectSparsity promoting regularization
dc.subjectSubsurface flow models
dc.titleEfficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates
dc.typeArticle
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
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
dc.contributor.institutionInstitute of Petroleum Engineering (IPE), Heriot-Watt University, Edinburgh Campus, Edinburgh, EH14 1AS, Scotland, United Kingdom
kaust.personHoteit, Ibrahim


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