Variance-based sensitivity analysis has been performed for a study of input parameters contribution into output variability of a largescale air pollution model the Unified Danish Eulerian Model. The problem of computing of numerical indicators of sensitivity Sobol’ global sensitivity indices leads to multidimensional integration. Plain and Adaptive Monte Carlo techniques for numerical integrati...

Sensitivity analysis aims at quantifying influence of input parameters dispersion on the output dispersion of a numerical model. When the model evaluation is time consuming, the computation of Sobol' indices based on Monte Carlo method is not applicable and a surrogate model has to be used. Among all approximation methods, polynomial chaos expansion is one of the most efficient to calculate var...

In systems biology, a common approach to model biological processes is to use large systems of nonlinear differential equations. The associated parameter estimation problem then requires a prior handling of the global identifiability question in a realistic experimental framework. The lack of a method able to solve this issue has indirectly encouraged the use of global sensitivity analysis to s...

Image synthesis techniques requiringMonteCarlo integration frequentlyneed to generate uniformly distributed samples within the unit hypercube. The ideal sample generation method will maximize the variance reduction as the numberof samples increases. Strati cationvia jittering is one simple, tractable, R. L. Cook. Stochastic sampling in computer graphics. ACM Transactions on Graphics, 5(1):51–72...

In the field of computer experiments sensitivity analysis aims at quantifying the relative importance of each input parameter (or combinations thereof) of a computational model with respect to the model output uncertainty. Variance decomposition methods leading to the well-known Sobol’ indices are recognized as accurate techniques, at a rather high computational cost though. The use of polynomi...