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Global sensitivity analysis using polynomial chaos expansion enhanced Gaussian process regression method
16 references·0 citing·37 second hop
Global sensitivity analysis using polynomial chaos expansion enhanced Gaussian process regression method (2023)Global sensitivity analysis using polynom…An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis (2022)An efficient multi-fidelity K…Surrogate-assisted global sensitivity analysis: an overview (2020)Surrogate-assisted global sen…Least squares polynomial chaos expansion: A review of sampling strategies (2017)Least squares polynomial chao…Global sensitivity analysis via multi-fidelity polynomial chaos expansion (2017)Global sensitivity analysis v…Global sensitivity analysis using support vector regression (2017)Global sensitivity analysis u…Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression (2017)Adaptive sparse polynomial ch…A Common Rationale for Global Sensitivity Measures and Their Estimation (2016)A Common Rationale for Global…Variable importance analysis: A comprehensive review (2015)Variable importance analysis:…Sensitivity analysis: A review of recent advances (2015)Sensitivity analysis: A revie…POLYNOMIAL-CHAOS-BASED KRIGING (2015)POLYNOMIAL-CHAOS-BASED KRIGINGAsymptotic normality and efficiency of two Sobol index estimators (2013)Asymptotic normality and effi…How to avoid a perfunctory sensitivity analysis (2010)How to avoid a perfunctory se…Polynomial chaos expansion for sensitivity analysis (2008)Polynomial chaos expansion fo…Calculations of Sobol indices for the Gaussian process metamodel (2008)Calculations of Sobol indices…Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global sensitivity indices fo…Stochastic Finite Elements: A Spectral Approach (1991)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)UQLab: A Framework for Uncertainty Quantification in Matlab (2014)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)Empirical Model Building and Response Surfaces (1988)Design and Analysis of Computer Experiments (1989)Better estimation of small sobol' sensitivity indices (2013)Global sensitivity analysis with dependence measures (2014)Sensitivity analysis for multidimensional and functional outputs (2014)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Global Sensitivity Analysis. The Primer (2007)From screening to quantitative sensitivity analysis. A unified approach (2011)Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices (2019)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion (2012)Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions (2017)Sensitivity and uncertainty analysis for flexoelectric nanostructures (2018)STATISTICS FOR SPATIAL DATA (1992)Making sense of global sensitivity analyses (2013)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)Risk, Ambiguity, and the Savage Axioms (1961)Factorial Sampling Plans for Preliminary Computational Experiments (1991)The Price of Robustness (2004)Risk assessment and risk management: Review of recent advances on their foundation (2015)A new uncertainty importance measure (2006)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)SALib: An open-source Python library for Sensitivity Analysis (2017)Rare Event Estimation Using Polynomial-Chaos Kriging (2016)Adaptive approaches in metamodel-based reliability analysis: A review (2020)Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (2021)New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability (2021)Global sensitivity analysis based on Gaussian‐process metamodelling for complex biomechanical problems (2022)
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References

  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
  • 2015Sensitivity analysis: A review of recent advances1,172 cited
  • 2010How to avoid a perfunctory sensitivity analysis1,000 cited
  • 2008Polynomial chaos expansion for sensitivity analysis747 cited
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  • 2008Calculations of Sobol indices for the Gaussian process metamodel331 cited
  • 2013Asymptotic normality and efficiency of two Sobol index estimators246 cited
  • 2020Surrogate-assisted global sensitivity analysis: an overview165 cited
  • 2017Least squares polynomial chaos expansion: A review of sampling strategies157 cited
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  • 2016A Common Rationale for Global Sensitivity Measures and Their Estimation119 cited
  • 2017Global sensitivity analysis using support vector regression114 cited
  • 2017Global sensitivity analysis via multi-fidelity polynomial chaos expansion

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  • 2022An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis79 cited
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