Application of QMC methods to PDEs with random coefficients : a survey of analysis and implementation

Handle URI:
http://hdl.handle.net/10754/624839
Title:
Application of QMC methods to PDEs with random coefficients : a survey of analysis and implementation
Authors:
Kuo, Frances; Dick, Josef; Le Gia, Thong; Nichols, James; Sloan, Ian; Graham, Ivan; Scheichl, Robert; Nuyens, Dirk; Schwab, Christoph
Abstract:
In this talk I will provide a survey of recent research efforts on the application of quasi-Monte Carlo (QMC) methods to PDEs with random coefficients. Such PDE problems occur in the area of uncertainty quantification. In recent years many papers have been written on this topic using a variety of methods. QMC methods are relatively new to this application area. I will consider different models for the randomness (uniform versus lognormal) and contrast different QMC algorithms (single-level versus multilevel, first order versus higher order, deterministic versus randomized). I will give a summary of the QMC error analysis and proof techniques in a unified view, and provide a practical guide to the software for constructing QMC points tailored to the PDE problems.
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
Issue Date:
5-Jan-2016
Type:
Presentation
Additional Links:
http://mediasite.kaust.edu.sa/Mediasite/Play/abc2a1b9170a4dce80cf174dad3b5f3b1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
Appears in Collections:
Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

Full metadata record

DC FieldValue Language
dc.contributor.authorKuo, Francesen
dc.contributor.authorDick, Josefen
dc.contributor.authorLe Gia, Thongen
dc.contributor.authorNichols, Jamesen
dc.contributor.authorSloan, Ianen
dc.contributor.authorGraham, Ivanen
dc.contributor.authorScheichl, Roberten
dc.contributor.authorNuyens, Dirken
dc.contributor.authorSchwab, Christophen
dc.date.accessioned2017-06-08T06:32:29Z-
dc.date.available2017-06-08T06:32:29Z-
dc.date.issued2016-01-05-
dc.identifier.urihttp://hdl.handle.net/10754/624839-
dc.description.abstractIn this talk I will provide a survey of recent research efforts on the application of quasi-Monte Carlo (QMC) methods to PDEs with random coefficients. Such PDE problems occur in the area of uncertainty quantification. In recent years many papers have been written on this topic using a variety of methods. QMC methods are relatively new to this application area. I will consider different models for the randomness (uniform versus lognormal) and contrast different QMC algorithms (single-level versus multilevel, first order versus higher order, deterministic versus randomized). I will give a summary of the QMC error analysis and proof techniques in a unified view, and provide a practical guide to the software for constructing QMC points tailored to the PDE problems.en
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/abc2a1b9170a4dce80cf174dad3b5f3b1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52en
dc.titleApplication of QMC methods to PDEs with random coefficients : a survey of analysis and implementationen
dc.typePresentationen
dc.conference.dateJanuary 5-10, 2016en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)en
dc.conference.locationKAUSTen
dc.contributor.institutionUniversity of New South Walesen
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