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Global sensitivity analysis using low-rank tensor approximations
16 references·16 citing·19 second hopsampled
Global sensitivity analysis using low-rank tensor approximations (2016)Global sensitivity analysis using low-ran…Sensitivity analysis: A review of recent advances (2015)Sensitivity analysis: A revie…A Review on Global Sensitivity Analysis Methods (2015)A Review on Global Sensitivit…UQLab: A Framework for Uncertainty Quantification in Matlab (2014)UQLab: A Framework for Uncert…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…An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis (2009)An adaptive algorithm to buil…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…A new uncertainty importance measure (2006)A new uncertainty importance …Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)Probabilistic Sensitivity Ana…The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Making best use of model evaluations to compute sensitivity indices (2002)Making best use of model eval…Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global sensitivity indices fo…A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)A Quantitative Model-Independ…Optimal Discretization of Random Fields (1993)Optimal Discretization of Ran…Factorial Sampling Plans for Preliminary Computational Experiments (1991)Factorial Sampling Plans for …A Review of Recent Advances in Surrogate Models for Uncertainty Quantification of High-Dimensional Engineering Applications (2024)A Review of Recent Advances i…A comparison between Sobol’s indices and Shapley’s effect for global sensitivity analysis of systems with independent input variables (2023)A comparison between Sobol’s …Geometric Moment-Dependent Global Sensitivity Analysis without Simulation Data: Application to Ship Hull Form Optimisation (2022)Geometric Moment-Dependent Gl…An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis (2022)An efficient multi-fidelity K…Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor (2021)Statistical developments for …AUTOMATIC SELECTION OF BASIS-ADAPTIVE SPARSE POLYNOMIAL CHAOS EXPANSIONS FOR ENGINEERING APPLICATIONS (2021)AUTOMATIC SELECTION OF BASIS-…A simple algorithm for global sensitivity analysis with Shapley effects (2021)A simple algorithm for global…Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (2020)Surrogate modeling of high-di…Global sensitivity analysis in high dimensions with PLS-PCE (2020)Global sensitivity analysis i…Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters (2020)Augmented sample-based approa…A Tutorial on Sobol’ Global Sensitivity Analysis Applied to Biological Models (2020)A Tutorial on Sobol’ Global S…Convergence of sensitivity analysis methods for evaluating combined influences of model inputs (2019)Convergence of sensitivity an…An expanded sparse Bayesian learning method for polynomial chaos expansion (2019)An expanded sparse Bayesian l…A framework for global reliability sensitivity analysis in the presence of multi-uncertainty (2019)A framework for global reliab…Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions (2018)Global sensitivity analysis i…Sequential Design of Experiment for Sparse Polynomial Chaos Expansions (2017)Sequential Design of Experime…Stochastic Finite Elements: A Spectral Approach (1991)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Statistics for Experimenters (1979)Handbook on Constructing Composite Indicators (2005)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Efficient computation of global sensitivity indices using sparse polynomial chaos expansions (2010)POLYNOMIAL-CHAOS-BASED KRIGING (2015)Design and Analysis of Computer Experiments (1989)How to avoid a perfunctory sensitivity analysis (2010)Risk, Ambiguity, and the Savage Axioms (1961)The Price of Robustness (2004)Risk assessment and risk management: Review of recent advances on their foundation (2015)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS (1946)On the Experimental Attainment of Optimum Conditions (1951)Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)
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References

  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 1991Factorial Sampling Plans for Preliminary Computational Experiments4,936 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 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
  • 1999A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output2,274 cited
  • 2002Making best use of model evaluations to compute sensitivity indices2,225 cited
  • 2010Adaptive sparse polynomial chaos expansion based on least angle regression1,507 cited
  • 2015Sensitivity analysis: A review of recent advances1,172 cited
  • 2015A Review on Global Sensitivity Analysis Methods1,113 cited
  • 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach1,085 cited
  • 2006A new uncertainty importance measure1,030 cited
  • 2014UQLab: A Framework for Uncertainty Quantification in Matlab1,018 cited
  • 2009An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis

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834 cited
  • 1993Optimal Discretization of Random Fields728 cited
  • Cited by

    • 2017Sequential Design of Experiment for Sparse Polynomial Chaos Expansions83 cited
    • 2024A Review of Recent Advances in Surrogate Models for Uncertainty Quantification of High-Dimensional Engineering Applications81 cited
    • 2022An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis79 cited
    • 2018Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions72 cited
    • 2023A comparison between Sobol’s indices and Shapley’s effect for global sensitivity analysis of systems with independent input variables67 cited
    • 2020Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square55 cited
    • 2020A Tutorial on Sobol’ Global Sensitivity Analysis Applied to Biological Models51 cited
    • 2021Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor47 cited
    • 2020Global sensitivity analysis in high dimensions with PLS-PCE37 cited
    • 2021AUTOMATIC SELECTION OF BASIS-ADAPTIVE SPARSE POLYNOMIAL CHAOS EXPANSIONS FOR ENGINEERING APPLICATIONS33 cited
    • 2021A simple algorithm for global sensitivity analysis with Shapley effects30 cited
    • 2019A framework for global reliability sensitivity analysis in the presence of multi-uncertainty27 cited
    • 2022Geometric Moment-Dependent Global Sensitivity Analysis without Simulation Data: Application to Ship Hull Form Optimisation26 cited
    • 2019An expanded sparse Bayesian learning method for polynomial chaos expansion23 cited
    • 2019Convergence of sensitivity analysis methods for evaluating combined influences of model inputs21 cited
    • 2020Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters20 cited