Bertrand Iooss

TL;DR: In this article, a review of various global sensitivity analysis methods of model output is presented, in a complete methodological framework, in which three kinds of methods are distinguished: the screening (coarse sorting of the most influential inputs among a large number), the measures of importance (quantitative sensitivity indices) and the deep exploration of the model behaviour (measuring the effects of inputs on their all variation range).

TL;DR: In this article, two approaches are studied to compute the Sobol indices: the first based on the predictor of the Gaussian process model and the second based on global stochastic process model.

TL;DR: A multidisciplinary group of researchers and practitioners revisit the current status of Sensitivity analysis, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems.

TL;DR: In this paper, the Gaussian process model which gives analytical expressions of Sobol indices is discussed, and the techniques are finally applied to a real case of hydrogeological modeling.

TL;DR: A specific estimation procedure is developed to adjust a Gaussian process model that is characterized by its mean and covariance functions in complex cases (non-linear relations, highly dispersed or discontinuous output, high-dimensional input, inadequate sampling designs, etc.).