Global sensitivity analysis is often impracticable for complex and resource intensive numerical models, as it requires a large number of runs. The metamodel approach replaces the original model by an approximated code that is much faster to run. This paper deals with the information loss in the estimation of sensitivity indices due to the metamodel approximation. A method for providing a robust error assessment is presented, hence enabling significant time savings without sacrificing on precision and rigor. The methodology is illustrated on two different types of metamodels: one based on reduced basis, the other one on RKHS interpolation.
Publié le : 2014-07-05
Classification:
Monte Carlo method,
sensitivity analysis,
reduced basis method,
Sobol indices,
bootstrap method,
Monte Carlo method.,
[STAT.CO]Statistics [stat]/Computation [stat.CO],
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST],
[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH],
[MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
@article{inria-00567977,
author = {Janon, Alexandre and Nodet, Ma\"elle and Prieur, Cl\'ementine},
title = {Uncertainties assessment in global sensitivity indices estimation from metamodels},
journal = {HAL},
volume = {2014},
number = {0},
year = {2014},
language = {en},
url = {http://dml.mathdoc.fr/item/inria-00567977}
}
Janon, Alexandre; Nodet, Maëlle; Prieur, Clémentine. Uncertainties assessment in global sensitivity indices estimation from metamodels. HAL, Tome 2014 (2014) no. 0, . http://gdmltest.u-ga.fr/item/inria-00567977/