Show simple item record

dc.contributor.authorLitvinenko, Alexander
dc.date.accessioned2017-05-23T06:04:12Z
dc.date.available2017-05-23T06:04:12Z
dc.date.issued2017-02-13
dc.identifier.urihttp://hdl.handle.net/10754/623694
dc.description.abstractWe explain how to solve stochastic PDE and then how to apply Bayesian Update formula to solve the coefficient inverse problem and to get more realistic posterior of uncertain coefficients.
dc.description.sponsorshipKAUST ECRC
dc.relation.urlhttp://www.hda2017.unsw.edu.au/
dc.subjectstochastic PDE with uncertain coefficients
dc.subjectBayesian formula
dc.subjectstochastic Galerkin
dc.subjectlow-rank tensor approximation
dc.titleLow-rank tensor methods for PDEs with uncertain coefficients and Bayesian Update surrogate
dc.typePresentation
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.conference.date13-17 February 2017
dc.conference.name7th Workshop on High-Dimensional Approximation
dc.conference.locationUNSW, Sydney, Australia
refterms.dateFOA2018-06-13T16:52:21Z


Files in this item

Thumbnail
Name:
Litvinenko_UNSW.pdf
Size:
2.628Mb
Format:
PDF
Description:
Talk given in UNSW University, Sydney, Australia

This item appears in the following Collection(s)

Show simple item record