Abstract
An important issue when large-scale mathematical models are used to support decision makers is their reliability. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is very significant for improving the reliability of these models. A comprehensive experimental study of Monte Carlo algorithm based on adaptive Monte Carlo approach for multidimensional numerical integration has been done. A comparison with Latin Hypercube Sampling and a particular quasi-Monte Carlo lattice rule based on generalized Fibonacci numbers has been presented. Such comparison has been made for the first time and this motivates the present study. The concentration values have been generated by the specialized modification SA-DEM of the Unified Danish Eulerian Model. Its parallel efficiency and scalability will be demonstrated by experiments on some of the most powerful supercomputers in Europe. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of six chemical reaction rates and four different groups of pollutants on the concentrations of important air pollutants.
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