The present paper deals with a new method of global sensitivity analysis of nonlinear models. This is based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction. Measures of importance in sensitivity analysis have been suggested by several authors, whose work is reviewed in this article. More emphasis is given to the...

Sobol’ indices measure the dependence of a high dimensional function on groups of variables defined on the unit cube [0, 1]. They are based on the ANOVA decomposition of functions, which is an L decomposition. In this paper we discuss generalizations of Sobol’ indices which yield L measures of the dependence of f on subsets of variables. Our interest is in values p > 2 because then variable imp...

Nanotechnology involves the ability to see and control individual atoms and molecules which are about 100 nanometer or smaller. One of the major tools used in this field is atomic force microscopy which uses a wealth of techniques to measure the topography and investigates the surface forces in nanoscale. Friction force is the representation of the surface interaction between two surfaces an...

Sensitivity analysis involves determining the contribution of individual input factors to uncertainty in model predictions. The most commonly used approach when doing a sensitivity analysis on spatial models is using Monte Carlo simulation. There are a number of techniques for calculating sensitivity indices from the Monte Carlo simulations, some more effective or efficient than others. These t...

We extend Atanassov’s methods for Halton sequences in two different directions: (1) in the direction of Niederreiter (t,s)−sequences, (2) in the direction of generating matrices for Halton sequences. It is quite remarkable that Atanassov’s method for classical Halton sequences applies almost “word for word” to (t,s)−sequences and gives an upper bound quite comparable to those of Sobol’, Faure a...

This paper introduces generalized Sobol’ indices, compares strategies for their estimation, and makes a systematic search for efficient estimators. Of particular interest are contrasts, sums of squares and indices of bilinear form which allow a reduced number of function evaluations compared to alternatives. The bilinear framework includes some efficient estimators from Saltelli (2002) and Maun...

Complex environmental models are controlled by a high number of parameters. Accurately estimating the values of all these parameters is almost impossible. Sensitivity analysis (SA) results enable the selection of the parameters to include in a calibration procedure, but can also assist in the identification of the model processes. Additionally, a sensitivity analysis can yield crucial informati...

Quasi-Monte Carlo (QMC) methods have been successfully used to compute high-dimensional integrals arising in many applications, especially in finance. To understand this success and the possible potential limitations of QMC, this paper focuses on quantitative measures of the quality of a point set in high dimensions. We introduce the order-`, superposition and truncation discrepancies, which me...