A study of highly efficient stochastic sequences for multidimensional sensitivity analysis
Ivan Dimov, Venelin Todorov, Karl K. Sabelfeld
Abstract
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.