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Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89
16 references·0 citing·34 second hop
Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89 (2018)Resampling strategies to improve surrogat…Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models (2017)Progressive Latin Hypercube S…Introduction to Sensitivity Analysis (2017)Introduction to Sensitivity A…Handbook of Uncertainty Quantification (2017)Handbook of Uncertainty Quant…Trends in sensitivity analysis practice in the last decade (2016)Trends in sensitivity analysi…Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties (2013)Numerical studies of space-fi…Global sensitivity analysis of stochastic computer models with joint metamodels (2011)Global sensitivity analysis o…Derivative based global sensitivity measures and their link with global sensitivity indices (2009)Derivative based global sensi…Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics (2008)Uncertainty Quantification an…Calculations of Sobol indices for the Gaussian process metamodel (2008)Calculations of Sobol indices…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Use of Kriging Models to Approximate Deterministic Computer Models (2005)Use of Kriging Models to Appr…Design and Modeling for Computer Experiments (2005)Design and Modeling for Compu…An importance quantification technique in uncertainty analysis for computer models (2002)An importance quantification …Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Comparison of Three Methods f…A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)A Comparison of Three Methods…Factorial Sampling Plans for Preliminary Computational Experiments (1991)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)From screening to quantitative sensitivity analysis. A unified approach (2011)Sensitivity analysis for volcanic source modeling quality assessment and model selection (2012)What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in E arth and E nvironmental systems models (2015)Stochastic Finite Elements: A Spectral Approach (1991)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Point-Collocation Nonintrusive Polynomial Chaos Method for Stochastic Computational Fluid Dynamics (2010)A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)STATISTICS FOR SPATIAL DATA (1992)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Asymptotic normality and efficiency of two Sobol index estimators (2013)Making sense of global sensitivity analyses (2013)Global sensitivity analysis with dependence measures (2014)Response Surface Methodology: Process and Product Optimization Using Designed Experiments (1996)Efficient surrogate models for reliability analysis of systems with multiple failure modes (2011)DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)Stochastic Kriging for Simulation Metamodeling (2009)A Review on Global Sensitivity Analysis Methods (2015)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)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)A distribution-free approach to inducing rank correlation among input variables (1982)Uniform Design: Theory and Application (2000)Importance measures in global sensitivity analysis of nonlinear models (1996)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems (2020)A Tutorial on Sobol’ Global Sensitivity Analysis Applied to Biological Models (2020)Modeling, analysis, and optimization under uncertainties: a review (2021)
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References

  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 1979A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code3,766 cited
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  • 2008Calculations of Sobol indices for the Gaussian process metamodel331 cited
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  • 2017Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models258 cited
  • 2002An importance quantification technique in uncertainty analysis for computer models251 cited
  • 2016Trends in sensitivity analysis practice in the last decade241 cited
  • 2013Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties

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155 cited
  • 2011Global sensitivity analysis of stochastic computer models with joint metamodels130 cited
  • 2017Introduction to Sensitivity Analysis125 cited
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