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Comparison of Surrogate-Based Uncertainty Quantification Methods for Computationally Expensive Simulators
16 references·11 citing·28 second hop
Comparison of Surrogate-Based Uncertainty Quantification Methods for Computationally Expensive Simulators (2017)Comparison of Surrogate-Based Uncertainty…DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)DiceKriging , DiceOptim : Two…Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Adaptive sparse polynomial ch…Choosing the Sample Size of a Computer Experiment: A Practical Guide (2009)Choosing the Sample Size of a…An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis (2009)An adaptive algorithm to buil…A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data (2008)A Sparse Grid Stochastic Coll…High-Order Collocation Methods for Differential Equations with Random Inputs (2005)High-Order Collocation Method…An adaptive multi-element generalized polynomial chaos method for stochastic differential equations (2005)An adaptive multi-element gen…Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)Probabilistic Sensitivity Ana…Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes (2004)Numerical Challenges in the U…Combining Field Data and Computer Simulations for Calibration and Prediction (2004)Combining Field Data and Comp…Modeling uncertainty in flow simulations via generalized polynomial chaos (2003)Modeling uncertainty in flow …A Stochastic Projection Method for Fluid Flow (2002)A Stochastic Projection Metho…A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A Comparison of Three Methods…STATISTICS FOR SPATIAL DATA (1992)STATISTICS FOR SPATIAL DATAStochastic Finite Elements: A Spectral Approach (1991)Stochastic Finite Elements: A…Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Challenges and opportunities in uncertainty quantification for healthcare and biological systems (2025)Challenges and opportunities …Fast Uncertainty Quantification by Sparse Data Learning From Multiphysics Systems (2023)Fast Uncertainty Quantificati…An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics (2022)An uncertainty-quantification…Uncertainty propagation of frequency response functions using a multi-output Gaussian Process model (2019)Uncertainty propagation of fr…Semi-intrusive uncertainty propagation for multiscale models (2019)Semi-intrusive uncertainty pr…Operator-Based Uncertainty Quantification of Stochastic Fractional Partial Differential Equations (2019)Operator-Based Uncertainty Qu…On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: Application to turbulence and fire-spotting model in wildland fire simulators (2019)On the merits of sparse surro…A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (2019)A data-driven framework for s…Review and comparison of two meta-model-based uncertainty propagation analysis methods in groundwater applications: polynomial chaos expansion and Gaussian process emulation (2018)Review and comparison of two …Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89 (2018)Resampling strategies to impr…Multi-Fidelity Uncertainty Analysis in CFD Using Hierarchical Kriging (2017)Multi-Fidelity Uncertainty An…The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)Efficient computation of global sensitivity indices using sparse polynomial chaos expansions (2010)UQLab: A Framework for Uncertainty Quantification in Matlab (2014)POLYNOMIAL-CHAOS-BASED KRIGING (2015)Structural Reliability Methods (1996)Global sensitivity analysis using polynomial chaos expansions (2007)Reliability-based design optimization using kriging surrogates and subset simulation (2011)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)LIF: A new Kriging based learning function and its application to structural reliability analysis (2016)Importance measures in global sensitivity analysis of nonlinear models (1996)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)A new uncertainty importance measure (2006)Bayesian analysis of computer code outputs: A tutorial (2006)How to avoid a perfunctory sensitivity analysis (2010)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (2007)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)A review of techniques for parameter sensitivity analysis of environmental models (1994)Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics (2008)DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual. (2010)Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization (2018)Fast Numerical Methods for Stochastic Computations: A Review (2009)Local Indicators of Spatial Association—LISA (1995)Methods to account for spatial autocorrelation in the analysis of species distributional data: a review (2007)Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2016)
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References

  • 1992STATISTICS FOR SPATIAL DATA9,140 cited
  • 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code8,233 cited
  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2005High-Order Collocation Methods for Differential Equations with Random Inputs1,522 cited
  • 2010Adaptive sparse polynomial chaos expansion based on least angle regression1,507 cited
  • 2003Modeling uncertainty in flow simulations via generalized polynomial chaos1,428 cited
  • 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach1,085 cited
  • 2008A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data942 cited
  • 2009An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis834 cited
  • 2009Choosing the Sample Size of a Computer Experiment: A Practical Guide766 cited
  • 2004Combining Field Data and Computer Simulations for Calibration and Prediction622 cited
  • 2012DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization594 cited
  • 2005An adaptive multi-element generalized polynomial chaos method for stochastic differential equations

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578 cited
  • 2002A Stochastic Projection Method for Fluid Flow503 cited
  • 2004Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes451 cited
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    • 2022An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics33 cited
    • 2017Multi-Fidelity Uncertainty Analysis in CFD Using Hierarchical Kriging25 cited
    • 2019On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: Application to turbulence and fire-spotting model in wildland fire simulators24 cited
    • 2025Challenges and opportunities in uncertainty quantification for healthcare and biological systems16 cited
    • 2018Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS8915 cited
    • 2019Semi-intrusive uncertainty propagation for multiscale models13 cited
    • 2023Fast Uncertainty Quantification by Sparse Data Learning From Multiphysics Systems10 cited
    • 2019Operator-Based Uncertainty Quantification of Stochastic Fractional Partial Differential Equations9 cited
    • 2019A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness8 cited