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Global sensitivity analysis using support vector regression
16 references·16 citing·27 second hopsampled
Global sensitivity analysis using support vector regression (2017)Global sensitivity analysis using support…Rare-event probability estimation with adaptive support vector regression surrogates (2016)Rare-event probability estima…Variable importance analysis: A comprehensive review (2015)Variable importance analysis:…Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes (2015)Metamodel-Based Sensitivity A…Efficient computation of global sensitivity indices using sparse polynomial chaos expansions (2010)Efficient computation of glob…Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Adaptive sparse polynomial ch…Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Variance based sensitivity an…Local Polynomial Estimation for Sensitivity Analysis on Models With Correlated Inputs (2009)Local Polynomial Estimation f…GUI–HDMR – A software tool for global sensitivity analysis of complex models (2008)GUI–HDMR – A software tool fo…Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…A new uncertainty importance measure (2006)A new uncertainty importance …Theorems and examples on high dimensional model representation (2003)Theorems and examples on high…Making best use of model evaluations to compute sensitivity indices (2002)Making best use of model eval…An importance quantification technique in uncertainty analysis for computer models (2002)An importance quantification …Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global sensitivity indices fo…Sensitivity analysis of an environmental model: an application of different analysis methods (1997)Sensitivity analysis of an en…Importance measures in global sensitivity analysis of nonlinear models (1996)Importance measures in global…Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: A review (2024)Dimensional decomposition-aid…Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis (2022)Optimal sparse polynomial cha…AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method (2021)AK-SESC: a novel reliability …AK-DS: An adaptive Kriging-based directional sampling method for reliability analysis (2021)AK-DS: An adaptive Kriging-ba…A global surrogate model for high-dimensional structural systems based on partial least squares and Kriging (2021)A global surrogate model for …An efficient method based on AK-MCS for estimating failure probability function (2020)An efficient method based on …A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems (2020)A global surrogate model tech…Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression (2019)Multi-level multi-fidelity sp…Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability (2019)Efficient methods by active l…An efficient method combining adaptive Kriging and fuzzy simulation for estimating failure credibility (2019)An efficient method combining…An efficient method combining active learning Kriging and Monte Carlo simulation for profust failure probability (2019)An efficient method combining…An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework (2019)An efficient and robust adapt…A coupled subset simulation and active learning kriging reliability analysis method for rare failure events (2019)A coupled subset simulation a…Using polynomial chaos expansion for uncertainty and sensitivity analysis of bridge structures (2018)Using polynomial chaos expans…A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices (2018)A Bayesian Monte Carlo-based …Sparse polynomial chaos expansion based on D-MORPH regression (2017)Sparse polynomial chaos expan…Sensitivity measures,anova-like Techniques and the use of bootstrap (1997)Efficient input—output model representations (1999)Estimating the approximation error when fixing unessential factors in global sensitivity analysis (2006)Sensitivity analysis for volcanic source modeling quality assessment and model selection (2012)Factorial Sampling Plans for Preliminary Computational Experiments (1991)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A simple and efficient method for global sensitivity analysis based on cumulative distribution functions (2015)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Statistics for Experimenters (1979)Design and Analysis of Computer Experiments (1989)Handbook on Constructing Composite Indicators (2005)An effective screening design for sensitivity analysis of large models (2007)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Stochastic Finite Elements: A Spectral Approach (1991)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)UQLab: A Framework for Uncertainty Quantification in Matlab (2014)POLYNOMIAL-CHAOS-BASED KRIGING (2015)Global Sensitivity Analysis. The Primer (2007)How to avoid a perfunctory sensitivity analysis (2010)SALib: An open-source Python library for Sensitivity Analysis (2017)Statistical principles in experimental design. (1962)Regression and Kriging metamodels with their experimental designs in simulation: A review (2016)Random forests for global sensitivity analysis: A selective review (2020)Surrogate-assisted global sensitivity analysis: an overview (2020)An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis (2018)Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (2021)
this work references cited by second hop· second hop is a sample of the most-cited connections

References

  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 2009Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index3,567 cited
  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
  • 2002Making best use of model evaluations to compute sensitivity indices2,225 cited
  • 1996Importance measures in global sensitivity analysis of nonlinear models2,014 cited
  • 2010Adaptive sparse polynomial chaos expansion based on least angle regression1,507 cited
  • 2006A new uncertainty importance measure1,030 cited
  • 2015Variable importance analysis: A comprehensive review526 cited
  • 2003Theorems and examples on high dimensional model representation450 cited
  • 2010Efficient computation of global sensitivity indices using sparse polynomial chaos expansions383 cited
  • 1997Sensitivity analysis of an environmental model: an application of different analysis methods292 cited
  • 2002An importance quantification technique in uncertainty analysis for computer models251 cited
  • 2008GUI–HDMR – A software tool for global sensitivity analysis of complex models210 cited
  • 2016Rare-event probability estimation with adaptive support vector regression surrogates210 cited

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  • 2015Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes126 cited
  • 2009Local Polynomial Estimation for Sensitivity Analysis on Models With Correlated Inputs125 cited
  • Cited by

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    • 2019Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability73 cited
    • 2024Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: A review70 cited
    • 2019A coupled subset simulation and active learning kriging reliability analysis method for rare failure events70 cited
    • 2018A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices65 cited
    • 2021AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method62 cited
    • 2020A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems62 cited
    • 2022Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis56 cited
    • 2019An efficient method combining adaptive Kriging and fuzzy simulation for estimating failure credibility51 cited
    • 2020An efficient method based on AK-MCS for estimating failure probability function51 cited
    • 2019Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression44 cited
    • 2019An efficient method combining active learning Kriging and Monte Carlo simulation for profust failure probability40 cited
    • 2019An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework34 cited
    • 2021A global surrogate model for high-dimensional structural systems based on partial least squares and Kriging32 cited