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Uncertainty quantification for multiscale simulations

  • B. DeVolder
  • , J. Glimm
  • , J. W. Grove
  • , Y. Kang
  • , Y. Lee
  • , K. Pao
  • , D. H. Sharp
  • , K. Ye
  • Los Alamos National Laboratory
  • Stony Brook University

Research output: Contribution to journalReview articlepeer-review

42 Scopus citations

Abstract

A general discussion of the quantification of uncertainty in numerical simulations is presented. A principal conclusion is that the distribution of solution errors is the leading term in the assessment of the validity of a simulation and its associated uncertainty in the Bayesian framework. Key issues that arise in uncertainty quantification are discussed for two examples drawn from shock wave physics and modeling of petroleum reservoirs. Solution error models, confidence intervals and Gaussian error statistics based on sim-lation studies are presented.

Original languageEnglish
Pages (from-to)29-41
Number of pages13
JournalJournal of Fluids Engineering, Transactions of the ASME
Volume124
Issue number1
DOIs
StatePublished - 2002

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