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dc.contributor.authorLitvinenko, Alexander
dc.date.accessioned2017-05-23T06:13:09Z
dc.date.available2017-05-23T06:13:09Z
dc.date.issued2017-03-05
dc.identifier.urihttp://hdl.handle.net/10754/623695
dc.description.abstractIn this work, we tried to show connections between Bayesian update and tensor completion techniques. Usually, only a small/sparse vector/tensor of measurements is available. The typical measurement is a function of the solution. The solution of a stochastic PDE is a tensor, the measurement as well. The idea is to use completion techniques to compute all "missing" values of the measurement tensor and only then apply the Bayesian technique.
dc.description.sponsorshipECRC KAUST, SRI UQ KAUST
dc.relation.urlhttp://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=61237
dc.subjectBayesian inference
dc.subjectBayesian update
dc.subjectposterior
dc.subjectmissing data
dc.subjectcompletion techniques
dc.subjectlow-rank tensors
dc.titleTensor completion for PDEs with uncertain coefficients and Bayesian Update
dc.typePresentation
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.conference.dateMarch 2017
dc.conference.nameSIAM CSE Conference
dc.conference.locationAtlanta , USA
refterms.dateFOA2018-06-13T16:54:58Z


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tensor completion for PDEs with uncertain coefficients and Bayesian update

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