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A computational multi-scale approach for the stochastic mechanical response of foam-filled honeycomb cores
7 references·1 citing·30 second hop
A computational multi-scale approach for the stochastic mechanical response of foam-filled honeycomb cores (2011)A computational multi-scale approach for …Gaussian process emulators for the stochastic finite element method (2011)Gaussian process emulators fo…Kriging Metamodeling in Simulation: A Review (2007)Kriging Metamodeling in Simul…Bayesian analysis of computer code outputs: A tutorial (2006)Bayesian analysis of computer…Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)Probabilistic Sensitivity Ana…Estimating Percentiles of Uncertain Computer Code Outputs (2004)Estimating Percentiles of Unc…The Design and Analysis of Computer Experiments (2003)The Design and Analysis of Co…Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Uncertainty analysis of honeycomb sandwich composite radome under imprecise probability (2022)Uncertainty analysis of honey…Maximum entropy sampling (1987)Exploratory designs for computational experiments (1995)Adaptive Designs of Experiments for Accurate Approximation of a Target Region (2010)A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation (2014)Design of Experiments (1936)On a Measure of the Information Provided by an Experiment (1956)Minimax and maximin distance designs (1990)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)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)How to avoid a perfunctory sensitivity analysis (2010)Sensitivity analysis: A review of recent advances (2015)Stochastic Finite Elements: A Spectral Approach (1991)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Stochastic structural dynamic analysis using Bayesian emulators (2013)An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions (2016)Uncertainty quantification in structural dynamic analysis using two-level Gaussian processes and Bayesian inference (2017)Simulation Modeling and Analysis. (1983)Statistics for Spatial Data (1993)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Space-Filling Designs for Computer Experiments (2003)Space-Filling Designs for Computer Experiments (2018)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Sensitivity Analysis for Importance Assessment (2002)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)An overview of methods to evaluate uncertainty of deterministic models in decision support (2014)
this work references cited by second hop

References

  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 2003The Design and Analysis of Computer Experiments2,219 cited
  • 2007Kriging Metamodeling in Simulation: A Review1,303 cited
  • 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach1,085 cited
  • 2006Bayesian analysis of computer code outputs: A tutorial756 cited
  • 2004Estimating Percentiles of Uncertain Computer Code Outputs79 cited
  • 2011Gaussian process emulators for the stochastic finite element method47 cited

Cited by

  • 2022Uncertainty analysis of honeycomb sandwich composite radome under imprecise probability8 cited

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