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A study of highly efficient stochastic sequences for multidimensional sensitivity analysis

Author

Listed:
  • Dimov Ivan

    (Department of Parallel Algorithms, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Block 25 A, 1113, Sofia, Bulgaria)

  • Todorov Venelin

    (Department of Information Modeling, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str., Block 8, 1113; and Department of Parallel Algorithms, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Block 25 A, 1113, Sofia, Bulgaria)

  • Sabelfeld Karl

    (Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Science, Novosibirsk, Russia)

Abstract

In this paper, we present and study highly efficient stochastic methods, including optimal super convergent methods for multidimensional sensitivity analysis of large-scale ecological models and digital twins. The computational efficiency (in terms of relative error and computational time) of the stochastic algorithms for multidimensional numerical integration has been studied to analyze the sensitivity of the digital ecosystem, namely the UNI-DEM model, which is particularly appropriate for connecting and orchestrating the many autonomous systems, infrastructures, platforms and data that constitute the bedrock of predicting and analyzing the consequences of possible climate changes. We deploy the digital twin paradigm in our consideration to study the output to variation of input emissions of the anthropogenic pollutants and to evaluate the rates of several chemical reactions.

Suggested Citation

  • Dimov Ivan & Todorov Venelin & Sabelfeld Karl, 2022. "A study of highly efficient stochastic sequences for multidimensional sensitivity analysis," Monte Carlo Methods and Applications, De Gruyter, vol. 28(1), pages 1-12, March.
    • Handle: RePEc:bpj:mcmeap:v:28:y:2022:i:1:p:1-12:n:4
    DOI: 10.1515/mcma-2022-2101
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