Use of Kriging Models to Approximate Deterministic Computer Models
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[1] D. Faddeev,et al. Computational Methods of Linear Algebra , 1959 .
[2] A. Goldberger. Best Linear Unbiased Prediction in the Generalized Linear Regression Model , 1962 .
[3] G. Matheron. Principles of geostatistics , 1963 .
[4] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..
[5] S. Shapiro,et al. An Analysis of Variance Test for Normality (Complete Samples) , 1965 .
[6] P. C. Gehlen,et al. Computer Experiments , 1996 .
[7] H. Akaike. A new look at the statistical model identification , 1974 .
[8] S. Geisser,et al. A Predictive Approach to Model Selection , 1979 .
[9] Calyampudi R. Rao. Handbook of statistics , 1980 .
[10] K. Mardia,et al. Maximum likelihood estimation of models for residual covariance in spatial regression , 1984 .
[11] G. Wahba. A Comparison of GCV and GML for Choosing the Smoothing Parameter in the Generalized Spline Smoothing Problem , 1985 .
[12] P. Kitanidis. Parameter Uncertainty in Estimation of Spatial Functions: Bayesian Analysis , 1986 .
[13] Brian D. Ripley,et al. Problems with likelihood estimation of covariance functions of spatial Gaussian processes , 1987 .
[14] C. Currin,et al. A Bayesian Approach to the Design and Analysis of Computer Experiments , 1988 .
[15] A. V. Vecchia. Estimation and model identification for continuous spatial processes , 1988 .
[16] B. Ripley. Statistical inference for spatial processes , 1990 .
[17] Kanti V. Mardia,et al. On multimodality of the likelihood in the spatial linear model , 1989 .
[18] Michael L. Stein,et al. A Comparison of Generalized Cross Validation and Modified Maximum Likelihood for Estimating the Parameters of a Stochastic Process , 1990 .
[19] G. Wahba. Spline models for observational data , 1990 .
[20] T. J. Mitchell,et al. Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments , 1991 .
[21] P. Royston. A Toolkit for Testing for Non‐Normality in Complete and Censored Samples , 1993 .
[22] Robert Haining,et al. Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .
[23] Hartmut Bossel,et al. Modeling and simulation , 1994 .
[24] R. Ababou,et al. On the condition number of covariance matrices in kriging, estimation, and simulation of random fields , 1994 .
[25] Geoffrey M. Laslett,et al. Kriging and Splines: An Empirical Comparison of their Predictive Performance in Some Applications , 1994 .
[26] Douglas C. Montgomery,et al. Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .
[27] P. Royston. A Remark on Algorithm as 181: The W‐Test for Normality , 1995 .
[28] Cristina H. Amon,et al. An engineering design methodology with multistage Bayesian surrogates and optimal sampling , 1996 .
[29] R. H. Myers,et al. Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .
[30] Art B. Owen,et al. 9 Computer experiments , 1996, Design and analysis of experiments.
[31] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[32] R. Storn,et al. Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .
[33] Max D. Morris,et al. Six factors which affect the condition number of matrices associated with kriging , 1997 .
[34] James P. Braselton,et al. Statistics with Mathematica , 1998 .
[35] David R. Anderson,et al. Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies , 1998 .
[36] Andrew Booker,et al. Design and analysis of computer experiments , 1998 .
[37] Timothy W. Simpson,et al. Comparison of Response Surface and Kriging Models in the Multidisciplinary Design of an Aerospike Nozzle , 1998 .
[38] Russell R. Barton,et al. Simulation metamodels , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).
[39] T. W. Layne,et al. A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models , 1998 .
[40] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[41] A. J. Booker,et al. A rigorous framework for optimization of expensive functions by surrogates , 1998 .
[42] Michael L. Stein,et al. Interpolation of spatial data , 1999 .
[43] M. D. McKay,et al. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .
[44] Timothy C. Coburn,et al. Geostatistics for Natural Resources Evaluation , 2000, Technometrics.
[45] T. Simpson,et al. Comparative studies of metamodeling techniques under multiple modeling criteria , 2000 .
[46] Roger Woodard,et al. Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.
[47] John E. Renaud,et al. MODELING AND SIMULATION UNCERTAINTY IN MULTIDISCIPLINARY DESIGN OPTIMIZATION , 2000 .
[48] Timothy W. Simpson,et al. Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.
[49] Martin Meckesheimer,et al. A FRAMEWORK FOR METAMODEL-BASED DESIGN: SUBSYSTEM METAMODEL ASSESSMENT AND IMPLEMENTATION ISSUES , 2001 .
[50] T. Simpson,et al. Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .
[51] Farrokh Mistree,et al. Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization , 2001 .
[52] Michael James Sasena,et al. Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. , 2002 .
[53] Timothy W. Simpson,et al. FACILITATING PROBABILISTIC MULTIDISCIPLINARY DESIGN OPTIMIZATION USING KRIGING APPROXIMATION MODELS , 2002 .
[54] T. Simpson,et al. Computationally Inexpensive Metamodel Assessment Strategies , 2002 .
[55] Jay D. Martin,et al. USE OF ADAPTIVE METAMODELING FOR DESIGN OPTIMIZATION , 2002 .
[56] Ruichen Jin,et al. On Sequential Sampling for Global Metamodeling in Engineering Design , 2002, DAC 2002.
[57] T. Simpson,et al. A Study on the Use of Kriging Models to Approximate Deterministic Computer Models , 2003, DAC 2003.
[58] Jack P. C. Kleijnen,et al. Kriging for interpolation in random simulation , 2003, J. Oper. Res. Soc..
[59] Cristina H. Amon,et al. Bayesian Surrogates Applied to Conceptual Stages of the Engineering Design Process , 2003 .
[60] Jay D. Martin,et al. On Using Kriging Models as Probabilistic Models in Design , 2004 .
[61] T. Simpson,et al. A Monte Carlo Simulation of the Kriging Model , 2004 .
[62] Ren-Jye Yang,et al. Approximation methods in multidisciplinary analysis and optimization: a panel discussion , 2004 .
[63] Timothy W. Simpson,et al. On the Use of Kriging Models to Approximate Deterministic Computer Models , 2004, DAC 2004.
[64] Ren-Jye Yang,et al. Metamodeling development for vehicle frontal impact simulation , 2001, DAC 2001.
[65] Raphael T. Haftka,et al. Surrogate-based Analysis and Optimization , 2005 .
[66] Brian D. Ripley,et al. Spatial Statistics: Ripley/Spatial Statistics , 2005 .
[67] Bryan Glaz,et al. Efficient Global Optimization of Helicopter Rotor Blades for Vibration Reduction in Forward Flight , 2006 .
[68] Jay D. Martin,et al. A Methodology to Manage System-level Uncertainty During Conceptual Design , 2006 .
[69] Daniel W. Apley,et al. Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments , 2006 .
[70] K. Kalniņš,et al. Metamodeling approach for analysis of post-buckling in composite panels with structural degradation , 2006 .
[71] Jay D. Martin,et al. A Monte Carlo Method for Reliability-Based Design Opitmization , 2006 .
[72] Shawn E. Gano,et al. Update strategies for kriging models used in variable fidelity optimization , 2006 .
[73] F. Scholz. Maximum Likelihood Estimation , 2006 .
[74] Salvador A. Pintos,et al. Toward an optimal ensemble of kernel-based approximations with engineering applications , 2006 .
[75] Wei Chen,et al. A non‐stationary covariance‐based Kriging method for metamodelling in engineering design , 2007 .
[76] G. Gary Wang,et al. Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .
[77] Kwang‐Yong Kim,et al. Evaluation of Surrogate Models in Optimization of Wire-Wrapped Fuel Assembly , 2007 .
[78] R. Haftka,et al. Ensemble of surrogates , 2007 .