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Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
16 references·0 citing·33 second hop
Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework (2017)Metamodel-based inverse uncertainty quant…Verification and Validation in Scientific Computing (2010)Verification and Validation i…Numerical Methods for Stochastic Computations: A Spectral Method Approach (2010)Numerical Methods for Stochas…Kriging Metamodeling in Simulation: A Review (2007)Kriging Metamodeling in Simul…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (2007)A Stochastic Collocation Meth…Survey of sampling-based methods for uncertainty and sensitivity analysis (2006)Survey of sampling-based meth…High-Order Collocation Methods for Differential Equations with Random Inputs (2005)High-Order Collocation Method…Modeling uncertainty in flow simulations via generalized polynomial chaos (2003)Modeling uncertainty in flow …Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Latin hypercube sampling and …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…Exploratory designs for computational experiments (1995)Exploratory designs for compu…STATISTICS FOR SPATIAL DATA (1992)STATISTICS FOR SPATIAL DATAStochastic Finite Elements: A Spectral Approach (1991)Stochastic Finite Elements: A…The origins of kriging (1990)The origins of krigingMinimax and maximin distance designs (1990)Minimax and maximin distance …Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Design of Experiments (1936)On a Measure of the Information Provided by an Experiment (1956)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)Random Balance Experimentation (1959)The Application of Random Balance Designs (1959)The art of simulation (1964)Factorial Sampling Plans for Preliminary Computational Experiments (1991)Optimum Designs in Regression Problems (1959)Designs for Computer Experiments (1989)Response Surface Methodology: A Retrospective and Literature Survey (2004)STIGLER'S LAW OF EPONYMY* (1980)Interpolation of Spatial Data (1999)Covariance Tapering for Interpolation of Large Spatial Datasets (2006)Fixed Rank Kriging for Very Large Spatial Data Sets (2008)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data (2008)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Empirical Model Building and Response Surfaces (1988)A Review on Global Sensitivity Analysis Methods (2015)Sensitivity analysis: A review of recent advances (2015)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)Global sensitivity analysis using polynomial chaos expansions (2007)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011)Imprecise probabilities in engineering analyses (2013)Best Practices for Scientific Computing (2014)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 DATA9,190 cited
  • 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code8,270 cited
  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems2,707 cited
  • 1990The origins of kriging1,823 cited
  • 2010Verification and Validation in Scientific Computing1,579 cited
  • 2005High-Order Collocation Methods for Differential Equations with Random Inputs1,522 cited
  • 1990Minimax and maximin distance designs1,452 cited
  • 2003Modeling uncertainty in flow simulations via generalized polynomial chaos1,428 cited
  • 2007A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data1,396 cited
  • 2007Kriging Metamodeling in Simulation: A Review1,303 cited
  • 2006Survey of sampling-based methods for uncertainty and sensitivity analysis1,240 cited
  • 1995Exploratory designs for computational experiments1,202 cited

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  • 2010Numerical Methods for Stochastic Computations: A Spectral Method Approach1,107 cited
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