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International Journal for Uncertainty Quantification
Impact-faktor: 4.911 5-jähriger Impact-Faktor: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Druckformat: 2152-5080
ISSN Online: 2152-5099

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International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2020032674
Forthcoming Article

Computation of Sobol indices in global sensitivity analysis from small data sets by probabilistic learning on manifolds

Maarten Arnst
University of Liege
Christian Soize
Université Paris-Est Marne-la-Vallée
Kevin Bulthuis
University of Liege

ABSTRAKT

Global sensitivity analysis provides insight into how sources of uncertainty contribute to uncertainty in predictions of computational models. Global sensitivity indices, also called variance-based sensitivity indices and Sobol indices, are most often computed with Monte Carlo methods. However, when the computational model is computationally expensive and only a small number of samples can be generated, that is, in so-called small-data settings, usual Monte Carlo estimates may lack sufficient accuracy. As a means of improving accuracy in such small-data settings, we explore the use of probabilistic learning. The objective of the probabilistic learning is to learn from the available samples a probabilistic representation that can be used to generate additional samples, from which Monte Carlo estimates of the global sensitivity indices are then deduced. We demonstrate the interest of such a probabilistic learning method by applying it in an illustration concerned with forecasting the contribution of the Antarctic ice sheet to sea-level rise.