Global sensitivity analysis of computer models with functional inputs

Bertrand Iooss and Mathieu Ribatet

Reliability Engineering and System Safety, 2009, vol. 94, issue 7, 1194-1204

Abstract: Global sensitivity analysis is used to quantify the influence of uncertain model inputs on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar model inputs. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Sobol's indices, when some model inputs are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary metamodeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked generalized linear models (GLMs) or generalized additive models (GAMs). The “mean model†allows to estimate the sensitivity indices of each scalar model inputs, while the “dispersion model†allows to derive the total sensitivity index of the functional model inputs. The proposed approach is compared to some classical sensitivity analysis methodologies on an analytical function. Lastly, the new methodology is applied to an industrial computer code that simulates the nuclear fuel irradiation.

Keywords: Sobol's indices; Joint modeling; Generalized additive model; Metamodel; Stochastic process; Uncertainty (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:94:y:2009:i:7:p:1194-1204

DOI: 10.1016/j.ress.2008.09.010

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