Sensitivity Analysis of High-Dimensional Models with Correlated Inputs
Additional information
Authors
Kardoš J.,
Edeling W.,
Suleimenova D.,
Groen D.,
Schenk O.
Type
Journal Article
Year
2025
Language
English
Abstract
Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input-output interactions. In many applications, the inputs are stochastically dependent, which violates one of the essential assumptions in the state-of-the-art sensitivity analysis methods. Consequently, the results obtained ignoring the correlations provide values which do not reflect the true contributions of the input parameters. This study proposes an approach to address the parameter correlations using a polynomial chaos expansion method and Rosenblatt and Cholesky transformations to reflect the parameter dependencies. Treatment of the correlated variables is discussed in context of variance and derivative-based sensitivity analysis. We demonstrate that the sensitivity of the correlated parameters can not only differ in magnitude, but even the sign of the derivative-based index can be inverted, thus significantly altering the model behavior compared to the prediction of the analysis disregarding the correlations. Numerous
experiments are conducted using workflow automation tools within the VECMA toolkit.
Keywords
Global sensitivity analysis, Uncertainty quantification, Parameter Correlation, Sobol index, Polynomial Chaos Expansion
Journal
Journal of Computational Science
Number ( Month )
July
Pages (or article number)
1-17
