Stochastic galerkin projection and numerical integration for stochastic systems of equations

Markus Wahlsten, Jan Nordström

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An incompletely parabolic system in three space dimensions with stochastic boundary and initial data is studied. The intrusive approach where we combine polynomial chaos with stochastic Galerkin projection is compared to the non-intrusive approach, where quadrature rules in combination with probability density functions of the prescribed uncertainties are used. The two methods are compared when calculating statistics for the compressible Navier-Stokes equations. As a measure of comparison, variance size, computational efficiency and accuracy are used.

Original languageEnglish
Title of host publicationUNCECOMP 2017 - Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
EditorsGeorge Stefanou, M. Papadrakakis, Vissarion Papadopoulos
PublisherNational Technical University of Athens
Pages426-440
Number of pages15
ISBN (Electronic)9786188284449
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2017 - Rhodes Island, Greece
Duration: 15 Jun 201717 Jun 2017

Publication series

NameUNCECOMP 2017 - Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
Volume2017-January

Conference

Conference2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2017
Country/TerritoryGreece
CityRhodes Island
Period15/06/1717/06/17

Keywords

  • Intrusive methods
  • Navier-stokes equations
  • Non-intrusive methods
  • Polynomial chaos
  • Stochastic data
  • Stochastic galerkin
  • Uncertainty quantification

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

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