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ArticleGlobal sensitivity analysis using support vector regression

Kai Cheng2017

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Articlebronze2017

Global sensitivity analysis using support vector regression

Kai Cheng · Zhenzhou Lü · Yicheng Zhou · Yuhao Wei

114 citations·0/0 files stored·Applied Mathematical Modelling

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Probabilistic and Robust Engineering Design Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference

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References

2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates 2009Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index 2007Global sensitivity analysis using polynomial chaos expansions 2002Making best use of model evaluations to compute sensitivity indices 1996Importance measures in global sensitivity analysis of nonlinear models 2010Adaptive sparse polynomial chaos expansion based on least angle regression 2006A new uncertainty importance measure 2015Variable importance analysis: A comprehensive review

Cited by

2021AK-DS: An adaptive Kriging-based directional sampling method for reliability analysis 2018Using polynomial chaos expansion for uncertainty and sensitivity analysis of bridge structures 2017Sparse polynomial chaos expansion based on D-MORPH regression 2019Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability 2024Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: A review

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Details

Year2017

Typearticle

Published2017-05-19

VenueApplied Mathematical Modelling

ISSN-L0307-904X

Languageen

Citations114

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.apm.2017.05.026

openalex_workW2616626757

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2019A coupled subset simulation and active learning kriging reliability analysis method for rare failure events

2018A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices

2021AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method