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A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method
8 references·3 citing·38 second hop
A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method (2019)A Kriging Surrogate Model for Uncertainty…Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data (2017)Kriging-based inverse uncerta…Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment (2015)Application of Latin Hypercub…DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)DiceKriging , DiceOptim : Two…AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)AK-MCS: An active learning re…Kriging metamodeling in simulation: A review (2007)Kriging metamodeling in simul…Use of Kriging Models to Approximate Deterministic Computer Models (2005)Use of Kriging Models to Appr…Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Latin hypercube sampling and …The origins of kriging (1990)The origins of krigingReal-world engineering problems: Two surrogate methods for robust vibration control of moving mass-beam coupling systems with epistemic uncertainty (2022)Real-world engineering proble…The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model (2021)The correlation between graph…A sequential reliability assessment and optimization strategy for multidisciplinary problems with active learning kriging model (2020)A sequential reliability asse…Design and Analysis of Computer Experiments (1989)STATISTICS FOR SPATIAL DATA (1992)Global Sensitivity Analysis. The Primer (2007)BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits (2014)Simulation optimization: a review of algorithms and applications (2015)Adaptive approaches in metamodel-based reliability analysis: A review (2020)Stochastic Finite Elements: A Spectral Approach (1991)Structural Reliability Methods (1996)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)Metamodel-based importance sampling for structural reliability analysis (2013)LIF: A new Kriging based learning function and its application to structural reliability analysis (2016)STIGLER'S LAW OF EPONYMY* (1980)Covariance Tapering for Interpolation of Large Spatial Datasets (2006)Fixed Rank Kriging for Very Large Spatial Data Sets (2008)Recent Advances in Bayesian Optimization (2023)Response Surface Methodology: Process and Product Optimization Using Designed Experiments (1996)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Calculations of Sobol indices for the Gaussian process metamodel (2008)Efficient surrogate models for reliability analysis of systems with multiple failure modes (2011)A distribution-free approach to inducing rank correlation among input variables (1982)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)Designs for Computer Experiments (1989)Efficient Model-Assisted Probability of Detection and Sensitivity Analysis for Ultrasonic Testing Simulations Using Stochastic Metamodeling (2019)Empirical Model Building and Response Surfaces (1988)A new uncertainty importance measure (2006)Survey of sampling-based methods for uncertainty and sensitivity analysis (2006)A Review on Global Sensitivity Analysis Methods (2015)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification (2019)Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach (2019)Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments (2021)Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design (2004)Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions (2008)A sequential radial basis function method for interval uncertainty analysis of multidisciplinary systems based on trust region updating scheme (2021)A novel BACG inverse reliability algorithm for efficient and robust reliability-based topology optimization of marine structures (2024)An integral method for Reliability-Based design Optimization (2025)Bayesian Experimental Design: A Review (1995)Sensitivity analysis: A review of recent advances (2015)
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References

  • 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems2,707 cited
  • 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation2,048 cited
  • 1990The origins of kriging1,763 cited
  • 2007Kriging metamodeling in simulation: A review1,278 cited
  • 2005Use of Kriging Models to Approximate Deterministic Computer Models840 cited
  • 2012DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization594 cited
  • 2017Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data75 cited
  • 2015Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment22 cited

Cited by

  • 2020A sequential reliability assessment and optimization strategy for multidisciplinary problems with active learning kriging model14 cited
  • 2021The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model6 cited
  • 2022Real-world engineering problems: Two surrogate methods for robust vibration control of moving mass-beam coupling systems with epistemic uncertainty6 cited

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