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DC Field | Value | Language |
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dc.contributor.author | Kardos, J | - |
dc.contributor.author | Edeling, W | - |
dc.contributor.author | Suleimenova, D | - |
dc.contributor.author | Groen, D | - |
dc.contributor.author | Schenk, O | - |
dc.date.accessioned | 2023-06-20T15:14:33Z | - |
dc.date.available | 2023-06-20T15:14:33Z | - |
dc.date.issued | 2023-05-31 | - |
dc.identifier | ORCID iDs: Diana Suleimenova https://orcid.org/0000-0003-4474-0943; Derek Groen https://orcid.org/0000-0001-7463-3765. | - |
dc.identifier | arXiv:2306.00555v1 [stat.ME] | - |
dc.identifier.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. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26698 | - |
dc.description | The file archived on this repository is a preprint. It has not been certified by peer review | en_US |
dc.description.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. | en_US |
dc.description.sponsorship | This research is part of the activities of the Innosuisse project no 34394.1 entitled “High-Performance Data Analytics Framework for Power Markets Simulation” which is financially supported by the Swiss Innovation Agency. This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID d120. DS and DG have been supported by the SEAVEA ExCALIBUR project, which has received funding from EPSRC under grant agreement EP/W007711/1. | - |
dc.format.extent | 1 - 17 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Cornell University | en_US |
dc.rights | Copyright The Authors 2023. arXiv preprint made available under a Creative Commons (CC BY-NC-SA) Attribution licence. | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/ | - |
dc.subject | global sensitivity analysis | en_US |
dc.subject | uncertainty quantification | en_US |
dc.subject | parameter correlation | en_US |
dc.subject | Sobol index | - |
dc.subject | polynomial chaos expansion | - |
dc.title | Sensitivity Analysis of High-Dimensional Models with Correlated Inputs | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.48550/arXiv.2306.00555 | - |
dc.identifier.eissn | 2331-8422 | - |
dc.rights.holder | The Authors | - |
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