Title: Variance-based sensitivity indices of computer models with dependant inputs: The Fourier amplitude sensitivity test
Authors: TARANTOLA STEFANOMARA THIERRY
Citation: INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION vol. 7 no. 6 p. 511-523
Publisher: BEGELL HOUSE INC
Publication Year: 2017
JRC N°: JRC109470
ISSN: 2152-5080
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC109470
DOI: 10.1615/Int.J.UncertaintyQuantification.2017020291
Type: Articles in periodicals and books
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.
JRC Directorate:Energy, Transport and Climate

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