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International Journal for Uncertainty Quantification
IF: 4.911 5-Year IF: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2017020291
pages 511-523

VARIANCE-BASED SENSITIVITY INDICES OF COMPUTER MODELS WITH DEPENDENT INPUTS: THE FOURIER AMPLITUDE SENSITIVITY TEST

S. Tarantola
Directorate for Energy, Transport and Climate, Joint Research Centre, European Commission, Ispra (VA), Italy, 21027
Thierry A. Mara
PIMENT, EA 4518, Université de La Réunion, FST, 15 Avenue René Cassin, 97715 Saint-Denis, Réunion; Directorate for Modelling, Indicators and Impact Evaluation, Joint Research Centre, European Commission, Ispra (VA), Italy, 21027

ABSTRACT

Several methods are proposed in the literature to perform global sensitivity analysis of computer models with independent inputs. Only a few allow for treating the case of dependent inputs. In the present work, we investigate how to compute variance-based sensitivity indices with the Fourier amplitude sensitivity test. This can be achieved with the help of the inverse Rosenblatt transformation or the inverse Nataf transformation. We illustrate this on two distinct benchmarks. As compared to the recent Monte Carlo based approaches recently proposed by the same authors [Mara, T.A., Tarantola, S., and Annoni, P., Non-parametric methods for global sensitivity analysis of model output with dependent inputs, Env. Model. Software, 72:173–183, 2015], the new approaches allow us to divide the computational effort by 2 to assess the entire set of first-order and total-order variance-based sensitivity indices.


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