# Certified metamodels for sensitivity indices estimation

Alexandre Janon; Maëlle Nodet; Clémentine Prieur

ESAIM: Proceedings (2012)

- Volume: 35, page 234-238
- ISSN: 1270-900X

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topJanon, Alexandre, Nodet, Maëlle, and Prieur, Clémentine. Denis Poisson, Fédération, and Trélat, E., eds. "Certified metamodels for sensitivity indices estimation." ESAIM: Proceedings 35 (2012): 234-238. <http://eudml.org/doc/251271>.

@article{Janon2012,

abstract = {Global sensitivity analysis of a numerical code, more specifically estimation of Sobol indices associated with input variables, generally requires a large number of model runs. When those demand too much computation time, it is necessary to use a reduced model (metamodel) to perform sensitivity analysis, whose outputs are numerically close to the ones of the original model, while being much faster to run. In this case, estimated indices are subject to two kinds of errors: sampling error, caused by the computation of the integrals appearing in the definition of the Sobol indices by a Monte-Carlo method, and metamodel error, caused by the replacement of the original model by the metamodel. In cases where we have certified bounds for the metamodel error, we propose a method to quantify both types of error, and we compute confidence intervals for first-order Sobol indices.},

author = {Janon, Alexandre, Nodet, Maëlle, Prieur, Clémentine},

editor = {Denis Poisson, Fédération, Trélat, E.},

journal = {ESAIM: Proceedings},

language = {eng},

month = {4},

pages = {234-238},

publisher = {EDP Sciences},

title = {Certified metamodels for sensitivity indices estimation},

url = {http://eudml.org/doc/251271},

volume = {35},

year = {2012},

}

TY - JOUR

AU - Janon, Alexandre

AU - Nodet, Maëlle

AU - Prieur, Clémentine

AU - Denis Poisson, Fédération

AU - Trélat, E.

TI - Certified metamodels for sensitivity indices estimation

JO - ESAIM: Proceedings

DA - 2012/4//

PB - EDP Sciences

VL - 35

SP - 234

EP - 238

AB - Global sensitivity analysis of a numerical code, more specifically estimation of Sobol indices associated with input variables, generally requires a large number of model runs. When those demand too much computation time, it is necessary to use a reduced model (metamodel) to perform sensitivity analysis, whose outputs are numerically close to the ones of the original model, while being much faster to run. In this case, estimated indices are subject to two kinds of errors: sampling error, caused by the computation of the integrals appearing in the definition of the Sobol indices by a Monte-Carlo method, and metamodel error, caused by the replacement of the original model by the metamodel. In cases where we have certified bounds for the metamodel error, we propose a method to quantify both types of error, and we compute confidence intervals for first-order Sobol indices.

LA - eng

UR - http://eudml.org/doc/251271

ER -

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