In high-dimensional problems, grouping input factors of similar sensitivity can help reduce the effective dimensionality of the factor space. This is useful ...
Oct 31, 2018 · We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models.
Jan 1, 2019 · We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models.
Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational ...
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... Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational ...
Jul 9, 2024 · We review the literature and summarize the available methods for monitoring and assessing convergence of sensitivity measures based on application purposes.
One of the most authoritative measures in global sensitivity analysis is the Sobol' total-order index, which can be computed with several different estimators.
Mar 21, 2022 · It develops a rational way to group factors/parameters together so as to effectively achieve a dimensionality reduction of the feature space, ...
Sheikholeslami, R. et al. (2019) “Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and ...
Global Sensitivity Analysis (GSA) examines model output sensitivity when varying all uncertain inputs across their entire range simultaneously (Saltelli ...