HAL: inria-00567977, version 3

DOI: 10.1615/Int.J.UncertaintyQuantification.2012004291

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International Journal for Uncertainty Quantification (2012)

Available versions

v1 (2011-02-23)

v2 (2011-11-16)

v3 (2012-06-15)

Uncertainties assessment in global sensitivity indices estimation from metamodels

Alexandre Janon (  Author to contact preferably ) 1, 2, Maëlle Nodet 1, Clémentine Prieur 1, 2

(2012)

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 approxima- tion. 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.

1:	MOISE (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann)
	CNRS : UMR5224 – INRIA – Laboratoire Jean Kuntzmann – Université Joseph Fourier - Grenoble I – Institut Polytechnique de Grenoble - Grenoble Institute of Technology
2:	GdR MASCOT-NUM ((Méthodes d'Analyse Stochastique des Codes et Traitements Numériques))
	CNRS : GDR3179

Domain

:

Statistics/Computation Mathematics/Statistics Statistics/Statistics Theory Mathematics/Numerical Analysis

Keywords: sensitivity analysis – reduced basis method – Sobol indices – bootstrap method – Monte Carlo method.

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From: Alexandre Janon <>

Submitted on: Friday, 15 June 2012 21:08:17

Updated on: Saturday, 23 February 2013 17:46:54