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Latin Hypercube Sampling-Based Monte Carlo Simulation: Extension of the Sample Size and Correlation Control
6 references·1 citing·25 second hop
Latin Hypercube Sampling-Based Monte Carlo Simulation: Extension of the Sample Size and Correlation Control (2018)Latin Hypercube Sampling-Based Monte Carl…Hierarchical Refinement of Latin Hypercube Samples (2014)Hierarchical Refinement of La…Correlation control in small-sample Monte Carlo type simulations I: A simulated annealing approach (2009)Correlation control in small-…Extension of Latin hypercube samples with correlated variables (2007)Extension of Latin hypercube …Refinement strategies for stratified sampling methods (2006)Refinement strategies for str…A study on algorithms for optimization of Latin hypercubes (2005)A study on algorithms for opt…Improvements to and limitations of Latin hypercube sampling (1998)Improvements to and limitatio…Robust Design Optimization of a Compressor Rotor Using Recursive Cokriging Based Multi-Fidelity Uncertainty Quantification and Multi-Fidelity Optimization (2024)Robust Design Optimization of…A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Design and Analysis of Computer Experiments (1989)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Reliability calculation of time-consuming problems using a small-sample artificial neural network-based response surface method (2016)Variance-based adaptive sequential sampling for Polynomial Chaos Expansion (2021)Efficient decoupling-assisted evolutionary/metaheuristic framework for expensive reliability-based design optimization problems (2022)A distribution-free approach to inducing rank correlation among input variables (1982)Generalized Latin Hypercube Design for Computer Experiments (2010)Space-filling Latin hypercube designs for computer experiments (2010)To Sobol or not to Sobol? The effects of sampling schemes in systems biology applications (2021)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Simulation of simply cross correlated random fields by series expansion methods (2007)FReET: Software for the statistical and reliability analysis of engineering problems and FReET-D: Degradation module (2013)Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification (2015)Response Surface Methodology (1996)Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling (2014)Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications (2019)Digital simulation of random processes and its applications (1972)Simulation of Stochastic Processes by Spectral Representation (1991)A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability (2016)A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise (1964)Simulation and the Monte Carlo Method (2007)Spectral representation-based neural network assisted stochastic structural mechanics (2014)A novel extension algorithm for optimized Latin hypercube sampling (2017)Efficient space-filling and near-orthogonality sequential Latin hypercube for computer experiments (2017)
this work references cited by second hop

References

  • 1998Improvements to and limitations of Latin hypercube sampling299 cited
  • 2005A study on algorithms for optimization of Latin hypercubes221 cited
  • 2009Correlation control in small-sample Monte Carlo type simulations I: A simulated annealing approach211 cited
  • 2007Extension of Latin hypercube samples with correlated variables113 cited
  • 2006Refinement strategies for stratified sampling methods84 cited
  • 2014Hierarchical Refinement of Latin Hypercube Samples57 cited

Cited by

  • 2024Robust Design Optimization of a Compressor Rotor Using Recursive Cokriging Based Multi-Fidelity Uncertainty Quantification and Multi-Fidelity Optimization11 cited

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