Control of modeling error in calibration and validation processes for predictive stochastic models

J. Tinsley Oden*, Serge Prudhomme

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The idea of adaptive control of modeling error is expanded to include ideas of statistical calibration, validation, and uncertainty quantification. © 2010 John Wiley & Sons, Ltd.
Original languageEnglish (US)
Pages (from-to)262-272
Number of pages11
JournalInternational Journal for Numerical Methods in Engineering
Volume87
Issue number1-5
DOIs
StatePublished - Sep 28 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): US00003
Acknowledgements: We have benefitted from numerous discussions with colleagues over recent years on the subjects of verification, validation, statistical inverse analysis, and uncertainty quantification. In particular, we thank Ivo Babuska, Omar Ghattas, Raul Tempone, Ernesto Prudencio, and Robert Moser for helping us develop our current but still developing understanding of those subjects. We also gratefully acknowledge support of our work under the DOE Multiscale Mathematics Contract DE-FG02-05ER25701, the DOE PSAAP Contract DE-FC52-08NA28615, and the KAUST Contract US00003, all with ICES.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

Fingerprint

Dive into the research topics of 'Control of modeling error in calibration and validation processes for predictive stochastic models'. Together they form a unique fingerprint.

Cite this