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Space-filling designs for computer experiments: A review
15 references·7 citing·24 second hop
Space-filling designs for computer experiments: A review (2016)Space-filling designs for computer experi…Maximum projection designs for computer experiments (2015)Maximum projection designs fo…Design and Analysis of Simulation Experiments (2015)Design and Analysis of Simula…ORTHOGONAL-MAXIMIN LATIN HYPERCUBE DESIGNS (2008)ORTHOGONAL-MAXIMIN LATIN HYPE…Design and Analysis of Simulation Experiments (2007)Design and Analysis of Simula…Design and Modeling for Computer Experiments (2005)Design and Modeling for Compu…An efficient algorithm for constructing optimal design of computer experiments (2004)An efficient algorithm for co…The Design and Analysis of Computer Experiments (2003)The Design and Analysis of Co…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…Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments (1998)Orthogonal Column Latin Hyper…Exploratory designs for computational experiments (1995)Exploratory designs for compu…Orthogonal Array-Based Latin Hypercubes (1993)Orthogonal Array-Based Latin …Minimax and maximin distance designs (1990)Minimax and maximin distance …Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Maximum entropy sampling (1987)Maximum entropy samplingComparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Comparison of Three Methods f…Surrogate modeling for probability distribution estimation: Uniform or adaptive design? (2025)Surrogate modeling for probab…Challenges and opportunities in uncertainty quantification for healthcare and biological systems (2025)Challenges and opportunities …Design of Experiments and machine learning for product innovation: A systematic literature review (2021)Design of Experiments and mac…On Design Orthogonality, Maximin Distance, and Projection Uniformity for Computer Experiments (2020)On Design Orthogonality, Maxi…LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model (2020)LowCon: A Design-based Subsam…Designing computer experiments with multiple types of factors: The MaxPro approach (2019)Designing computer experiment…A framework for probabilistic model-based engineering and data synthesis (2019)A framework for probabilistic…THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS (1946)Algorithmic construction of optimal symmetric Latin hypercube designs (2000)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Design of computer experiments: space filling and beyond (2011)Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence (2012)Design of computer experiments: A review (2017)On a Measure of the Information Provided by an Experiment (1956)Proceedings of the 2002 Winter Simulation Conference (2002)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)Stochastic Kriging for Simulation Metamodeling (2009)A Review on Global Sensitivity Analysis Methods (2015)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)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)Response Surface Methodology: A Retrospective and Literature Survey (2004)Choosing the Sample Size of a Computer Experiment: A Practical Guide (2009)A distribution-free approach to inducing rank correlation among input variables (1982)Factorial Sampling Plans for Preliminary Computational Experiments (1991)Uniform Design: Theory and Application (2000)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)Sensitivity analysis: A review of recent advances (2015)
this work references cited by second hop

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
  • 1979Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code3,238 cited
  • 2003The Design and Analysis of Computer Experiments2,204 cited
  • 1990Minimax and maximin distance designs1,452 cited
  • 1995Exploratory designs for computational experiments1,202 cited
  • 2005Design and Modeling for Computer Experiments1,135 cited
  • 1993Orthogonal Array-Based Latin Hypercubes859 cited
  • 2004An efficient algorithm for constructing optimal design of computer experiments671 cited
  • 1987Maximum entropy sampling603 cited
  • 1998Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments461 cited
  • 2015Design and Analysis of Simulation Experiments356 cited
  • 2015Maximum projection designs for computer experiments339 cited
  • 2007Design and Analysis of Simulation Experiments317 cited
  • 2008ORTHOGONAL-MAXIMIN LATIN HYPERCUBE DESIGNS266 cited

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Cited by

  • 2021Design of Experiments and machine learning for product innovation: A systematic literature review96 cited
  • 2020On Design Orthogonality, Maximin Distance, and Projection Uniformity for Computer Experiments54 cited
  • 2019Designing computer experiments with multiple types of factors: The MaxPro approach52 cited
  • 2020LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model48 cited
  • 2025Surrogate modeling for probability distribution estimation: Uniform or adaptive design?18 cited
  • 2025Challenges and opportunities in uncertainty quantification for healthcare and biological systems16 cited
  • 2019A framework for probabilistic model-based engineering and data synthesis11 cited