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

J. Tinsley Oden; Serge Prudhomme

doi:10.1002/nme.3038

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 journal › Article › peer-review

15 Scopus citations

Abstract

The idea of adaptive control of modeling error is expanded to include ideas of statistical calibration, validation, and uncertainty quantification.

Original language	English (US)
Pages (from-to)	262-272
Number of pages	11
Journal	International Journal for Numerical Methods in Engineering
Volume	87
Issue number	1-5
DOIs	https://doi.org/10.1002/nme.3038
State	Published - Jul 8 2011
Externally published	Yes

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.

Keywords

Adaptive modeling
Bayesian approaches
Error estimation
Quantities of interest

ASJC Scopus subject areas

General Engineering
Applied Mathematics
Numerical Analysis

Access to Document

10.1002/nme.3038

Cite this

@article{9e2ec38c9db34b51beab60963a4afad2,

title = "Control of modeling error in calibration and validation processes for predictive stochastic models",

abstract = "The idea of adaptive control of modeling error is expanded to include ideas of statistical calibration, validation, and uncertainty quantification.",

keywords = "Adaptive modeling, Bayesian approaches, Error estimation, Quantities of interest",

author = "Oden, {J. Tinsley} and Serge Prudhomme",

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.",

year = "2011",

month = jul,

day = "8",

doi = "10.1002/nme.3038",

language = "English (US)",

volume = "87",

pages = "262--272",

journal = "International Journal for Numerical Methods in Engineering",

issn = "0029-5981",

publisher = "John Wiley and Sons Ltd",

number = "1-5",

}

TY - JOUR

T1 - Control of modeling error in calibration and validation processes for predictive stochastic models

AU - Oden, J. Tinsley

AU - Prudhomme, Serge

N1 - 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.

PY - 2011/7/8

Y1 - 2011/7/8

N2 - The idea of adaptive control of modeling error is expanded to include ideas of statistical calibration, validation, and uncertainty quantification.

AB - The idea of adaptive control of modeling error is expanded to include ideas of statistical calibration, validation, and uncertainty quantification.

KW - Adaptive modeling

KW - Bayesian approaches

KW - Error estimation

KW - Quantities of interest

UR - http://www.scopus.com/inward/record.url?scp=79958743293&partnerID=8YFLogxK

U2 - 10.1002/nme.3038

DO - 10.1002/nme.3038

M3 - Article

AN - SCOPUS:79958743293

SN - 0029-5981

VL - 87

SP - 262

EP - 272

JO - International Journal for Numerical Methods in Engineering

JF - International Journal for Numerical Methods in Engineering

IS - 1-5

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this