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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Acknowledgements
Venelin Todorov is supported by the Bulgarian National Science Fund under Project KP-06-M32/2 - 17.12.2019 “Advanced Stochastic and Deterministic Approaches for Large-Scale Problems of Computational Mathematics” and by the National Scientific Program “Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICT in SES)”, contract No D01—205/23.11.2018, financed by the Ministry of Education and Science in Bulgaria. This work is partially supported by the Bulgarian National Science Fund under Project DN 12/5-2017 and DN 12/4-2017.
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Ostromsky, T., Todorov, V., Dimov, I., Zlatev, Z. (2021). Sensitivity Studies of an Air Pollution Model by Using Efficient Stochastic Algorithms for Multidimensional Numerical Integration. In: Dimov, I., Fidanova, S. (eds) Advances in High Performance Computing. HPC 2019. Studies in Computational Intelligence, vol 902. Springer, Cham. https://doi.org/10.1007/978-3-030-55347-0_16
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