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Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data
16 references·16 citing·30 second hopsampled
Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data (2017)Kriging-based inverse uncertainty quantif…LIF: A new Kriging based learning function and its application to structural reliability analysis (2016)LIF: A new Kriging based lear…UQLab: A Framework for Uncertainty Quantification in Matlab (2014)UQLab: A Framework for Uncert…Uncertainty Quantification: Theory, Implementation, and Applications (2013)Uncertainty Quantification: T…DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)DiceKriging , DiceOptim : Two…Verification and Validation in Scientific Computing (2010)Verification and Validation i…DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual. (2010)DAKOTA : a multilevel paralle…Kriging metamodeling in simulation: A review (2007)Kriging metamodeling in simul…Bayesian analysis of computer code outputs: A tutorial (2006)Bayesian analysis of computer…Use of Kriging Models to Approximate Deterministic Computer Models (2005)Use of Kriging Models to Appr…Combining Field Data and Computer Simulations for Calibration and Prediction (2004)Combining Field Data and Comp…Interpolation of Spatial Data: Some Theory for Kriging (2000)Interpolation of Spatial Data…Interpolation of Spatial Data: Some Theory for Kriging (2000)Interpolation of Spatial Data…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, Revised Edition. (1994)Statistics for Spatial Data, …Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…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…Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data (2024)Functional PCA and deep neura…Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach (2023)Uncertainty Quantification fo…Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models (2022)Quantification of Deep Neural…Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments (2021)Uncertainty quantification fo…Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing (2021)Towards improving the predict…Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues (2021)Reliability Assessment of Pas…Inverse uncertainty quantification for imprecise structure based on evidence theory and similar system analysis (2021)Inverse uncertainty quantific…Computational methods for the robust optimization of the design of a dynamic aerospace system in the presence of aleatory and epistemic uncertainties (2021)Computational methods for the…Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data (2021)Bayesian inverse uncertainty …Time-dependent reliability prediction using transfer learning (2020)Time-dependent reliability pr…Industrial issues and solutions to statistical model improvement: a case study of an automobile steering column (2020)Industrial issues and solutio…Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments (2019)Validation and uncertainty qu…Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach (2019)Uncertainty quantification of…Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification (2019)Demonstration of the relation…A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method (2019)A Kriging Surrogate Model for…Propagation of material behavior uncertainty in a nonlinear finite element model of reconstructive surgery (2018)Propagation of material behav…Global sensitivity analysis using polynomial chaos expansions (2007)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis (2018)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses (2022)STATISTICS FOR SPATIAL DATA (1992)Response Surface Methodology: Process and Product Optimization Using Designed Experiments (1996)Calculations of Sobol indices for the Gaussian process metamodel (2008)Efficient surrogate models for reliability analysis of systems with multiple failure modes (2011)Statistics for Spatial Data. (1993)Model-Based Geostatistics (1998)Spatial Panel Econometrics (2008)Bayesian Experimental Design: A Review (1995)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)Sensitivity analysis: A review of recent advances (2015)A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011)Imprecise probabilities in engineering analyses (2013)Combustion kinetic model uncertainty quantification, propagation and minimization (2014)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)Importance measures in global sensitivity analysis of nonlinear models (1996)SPATIAL AUTOCORRELATION AND AUTOREGRESSIVE MODELS IN ECOLOGY (2002)Spatial analysis a guide for ecologists (2005)Fixed Rank Kriging for Very Large Spatial Data Sets (2008)A unified framework for stochastic optimization (2018)Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey (2020)A state-of-the-art review on uncertainty analysis of rotor systems (2022)
this work references cited by second hop· second hop is a sample of the most-cited connections

References

  • 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
  • 1979A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code3,766 cited
  • 2000Interpolation of Spatial Data: Some Theory for Kriging1,916 cited
  • 1994Statistics for Spatial Data, Revised Edition.1,875 cited
  • 2010Verification and Validation in Scientific Computing1,551 cited
  • 2007Kriging metamodeling in simulation: A review1,278 cited
  • 2013Uncertainty Quantification: Theory, Implementation, and Applications1,147 cited
  • 2014UQLab: A Framework for Uncertainty Quantification in Matlab1,018 cited
  • 2005Use of Kriging Models to Approximate Deterministic Computer Models840 cited
  • 2000Interpolation of Spatial Data: Some Theory for Kriging779 cited
  • 2010DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual.775 cited
  • 2006Bayesian analysis of computer code outputs: A tutorial756 cited
  • 2004Combining Field Data and Computer Simulations for Calibration and Prediction622 cited

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  • 2012DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization594 cited
  • 2016LIF: A new Kriging based learning function and its application to structural reliability analysis444 cited
  • Cited by

    • 2021Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments73 cited
    • 2019Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach59 cited
    • 2019Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification44 cited
    • 2021Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues35 cited
    • 2018Propagation of material behavior uncertainty in a nonlinear finite element model of reconstructive surgery35 cited
    • 2019Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments34 cited
    • 2022Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models28 cited
    • 2020Time-dependent reliability prediction using transfer learning21 cited
    • 2024Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data20 cited
    • 2019A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method18 cited
    • 2021Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data17 cited
    • 2021Inverse uncertainty quantification for imprecise structure based on evidence theory and similar system analysis17 cited
    • 2020Industrial issues and solutions to statistical model improvement: a case study of an automobile steering column15 cited
    • 2021Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing15 cited
    • 2023Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach14 cited
    • 2021Computational methods for the robust optimization of the design of a dynamic aerospace system in the presence of aleatory and epistemic uncertainties13 cited