Sensitivity Analysis for Nonlinear Mathematical Models. Numerical ExperienceSensitivity Analysis for Nonlinear Mathematical Models. Numerical Experience

By Saltelli Andrea, S. I.M.. 1995

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Citations in the corpus

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, unknown): “Variance-based methods use a variance ratio to estimate the importance of parameters with the foundation of variance decomposition (Saltelli et al., 1999; Sobol’, 1993).”
An Intercomparison of Sampling Methods for Uncertainty Quantification of Environmental Dynamic Models (2015, methodology): “As described in (Sobol', 1993), the Sobol’ g-function is a benchmark test function for sensitivity analysis.”
A randomized orthogonal array-based procedure for the estimation of first- and second-order Sobol' indices (2015, unknown): “Sobol’ indices (SI) [1] are quantities defined by normalizing variance components in an ANOVA decomposition [1, 2, 3].”
On Sequential Sampling for Global Metamodeling in Engineering Design (2002, methodology): “However, once the relative importance information is available, e.g., through methods such as sensitivity analysis discussed by Sobol (1993), this approach can be easily used for RBF or other metamodeling techniques.”
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, methodology): “Among various global sensitivity analysis methods, variance-based methods (Sobol’, 1993; Saltelli et al., 1999) have gained popularity (Massmann and Holzmann, 2012; van Werkhoven et al., 2008; Wagener et al., 2009; Yang, 2011; Zhang et al., 2013a, 2013b).”