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A study of highly efficient stochastic sequences for multidimensional sensitivity analysis

  • Ivan Dimov , Venelin Todorov and Karl Sabelfeld ORCID logo EMAIL logo
Published/Copyright: February 15, 2022

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.

MSC 2010: 65C05; 93B35

Award Identifier / Grant number: 20-51-18009

Award Identifier / Grant number: KP-06-M32/2-17.12.2019

Award Identifier / Grant number: KP-06-N52/5

Funding statement: The study on multidimensional sensitivity analysis is supported by the RFBR and NSFB, project number 20-51-18009. 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 Bulgarian National Science Fund under Project KP-06-N52/5, “Efficient methods for modeling, optimization and decision making”.

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Received: 2021-10-28
Revised: 2022-01-22
Accepted: 2022-01-24
Published Online: 2022-02-15
Published in Print: 2022-03-01

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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