Abstract
Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is very significant for improving the reliability of these models. Several efficient quazi-Monte Carlo algorithms – van der Corput sequence and Fibonacci based lattice rule have been used in our sensitivity studies of the model output results for some air pollutants with respect to the emission levels and some chemical reactions rates. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants.
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Acknowledgement
Venelin Todorov is supported by the Bulgarian National Science Fund under Projects KP-06-N52/5 “Efficient methods for modeling, optimization and decision making” and KP-06-N62/6 “Machine learning through physics-informed neural networks”. The work is also supported by the Project KP-06-Russia/17 “New Highly Efficient Stochastic Simulation Methods and Applications”.
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Todorov, V., Dimov, I., Ostromsky, T., Apostolov, S., Dimitrov, Y., Zlatev, Z. (2023). Quasi-Monte Carlo Methods Based on Low Discrepancy Sequences for Sensitivity Analysis in Air Pollution Modelling. In: Georgiev, I., Kostadinov, H., Lilkova, E. (eds) Advanced Computing in Industrial Mathematics. BGSIAM 2019. Studies in Computational Intelligence, vol 1111. Springer, Cham. https://doi.org/10.1007/978-3-031-42010-8_22
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