Handle URI:
http://hdl.handle.net/10754/623975
Title:
Data-Driven Model Order Reduction for Bayesian Inverse Problems
Authors:
Cui, Tiangang; Youssef, Marzouk; Willcox, Karen
Abstract:
One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
Issue Date:
6-Jan-2014
Type:
Poster
Appears in Collections:
Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

Full metadata record

DC FieldValue Language
dc.contributor.authorCui, Tiangangen
dc.contributor.authorYoussef, Marzouken
dc.contributor.authorWillcox, Karenen
dc.date.accessioned2017-06-01T10:20:42Z-
dc.date.available2017-06-01T10:20:42Z-
dc.date.issued2014-01-06-
dc.identifier.urihttp://hdl.handle.net/10754/623975-
dc.description.abstractOne of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.en
dc.subjectBayesianen
dc.titleData-Driven Model Order Reduction for Bayesian Inverse Problemsen
dc.typePosteren
dc.conference.dateJanuary 6-10, 2014en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)en
dc.conference.locationKAUSTen
dc.contributor.institutionMassachusetts Institute of Technologyen
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