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Title: | Sensitivity Analysis of High-Dimensional Models with Correlated Inputs |
Authors: | Kardos, J Edeling, W Suleimenova, D Groen, D Schenk, O |
Keywords: | global sensitivity analysis;uncertainty quantification;parameter correlation;Sobol index;polynomial chaos expansion |
Issue Date: | 31-May-2023 |
Publisher: | Cornell University |
Citation: | Kardos, J. et al. (202x) 'Sensitivity Analysis of High-Dimensional Models with Correlated Inputs', arXiv:2306.00555v1 [stat.ME], pp. 1 - 17. doi: 10.48550/arXiv.2306.00555. |
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. |
Description: | The file archived on this repository is a preprint. It has not been certified by peer review |
URI: | https://bura.brunel.ac.uk/handle/2438/26698 |
DOI: | https://doi.org/10.48550/arXiv.2306.00555 |
Other Identifiers: | ORCID iDs: Diana Suleimenova https://orcid.org/0000-0003-4474-0943; Derek Groen https://orcid.org/0000-0001-7463-3765. arXiv:2306.00555v1 [stat.ME] |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Preprint.pdf | Copyright The Authors 2023. arXiv preprint made available under a Creative Commons (CC BY-NC-SA) Attribution licence. | 2.87 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License