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International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

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ISSN Печать: 2152-5080

ISSN Онлайн: 2152-5099

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ROBUSTNESS OF THE SOBOL' INDICES TO DISTRIBUTIONAL UNCERTAINTY

Том 9, Выпуск 5, 2019, pp. 453-469
DOI: 10.1615/Int.J.UncertaintyQuantification.2019030553
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Краткое описание

Global sensitivity analysis (GSA) is used to quantify the influence of uncertain variables in a mathematical model. Prior to performing GSA, the user must specify (or implicitly assume) a probability distribution to model the uncertainty, and possibly statistical dependencies, of the variables. Determining this distribution is challenging in practice as the user has limited and imprecise knowledge of the uncertain variables. This paper analyzes the robustness of the Sobol' indices, a commonly used tool in GSA, to changes in the distribution of the uncertain variables. A method for assessing such robustness is developed that requires minimal user specification and no additional evaluations of the model. Theoretical and computational aspects of the method are considered and illustrated through examples.

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ЦИТИРОВАНО В
  1. Zhang Jiaxin, TerMaath Stephanie, Shields Michael D., Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling, Mechanical Systems and Signal Processing, 148, 2021. Crossref

  2. Fort Jean-Claude, Klein Thierry, Lagnoux Agnès, Global Sensitivity Analysis and Wasserstein Spaces, SIAM/ASA Journal on Uncertainty Quantification, 9, 2, 2021. Crossref

  3. Qian George, Mahdi Adam, Sensitivity analysis methods in the biomedical sciences, Mathematical Biosciences, 323, 2020. Crossref

  4. Sumner Tom, White Richard G., Variance-based sensitivity analysis of tuberculosis transmission models, Journal of The Royal Society Interface, 19, 196, 2022. Crossref

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