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( a, b) The filling of a 2D parameter space is conceptually shown for five samples. Risk Assess.Global sensitivity analysis using Latin hypercube sampling (LHS). Volkova, E., Iooss, B., Van Dorpe, F.: Global sensitivity analysis for a numerical model of radionuclide migration from the RRC “Kurchatov Institute” radwaste disposal site. Stein, M.: Large sample properties of simulations using Latin hypercube sampling. Sobol, I.: Uniformly distributed sequences with additional uniformity property. Simpson, T., Peplinski, J., Kock, P., Allen, J.: Metamodel for computer-based engineering designs: Survey and recommendations. Simpson, T., Lin, D., Chen, W.: Sampling strategies for computer experiments: Design and analysis. Wiley Series in Probability and Statistics. Pistone, G., Vicario, G.: Comparing and generating Latin Hypercube designs in Kriging models. In: Actes de MATERIAUX, Dijon, France, 2006 Petelet, M., Asserin, O., Iooss, B., Loredo, A.: Echantillonnage LHS des propriétés matériau des aciers pour l’analyse de sensibilité globale en simulation numérique du soudage. Thèse de l’Université de Bourgogne (2007)
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Petelet, M.: Analyse de sensibilité globale de modèles thermomécaniques de simulation numérique du soudage. Park, J.-S.: Optimal Latin-hypercube designs for computer experiments. Owen, A.: A central limit theorem for Latin hypercube sampling. McKay, M., Beckman, R., Conover, W.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Levy, S., Steinberg, D.: Computer experiments: A review. Kurowicka, D., Cooke, R.: Uncertainty Analysis with High Dimensional Dependence Modelling. Kleijnen, J.: Design and Analysis of Simulation Experiments.
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Jourdan, A., Franco, J.: Optimal Latin hypercube designs for the Kullback-Leibler criterion. Iooss, B., Boussouf, L., Feuillard, V., Marrel, A.: Numerical studies of the metamodel fitting and validation processes. Iman, R., Conover, W.: A distribution-free approach to inducing rank correlation among input variables. Helton, J., Davis, F.: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Gentle, J.: Random Number Generation and Monte Carlo Methods. Wiley, New York (2008)įang, K.-T., Li, R., Sudjianto, A.: Design and Modeling for Computer Experiments. (eds.): Uncertainty in Industrial Practice. 28, 667–680 (2008)īursztyn, D., Steinberg, D.: Comparison of designs for computer experiments. Available at URL: īorgonovo, E.: Sensitivity analysis of model output with input constraints: A generalized rationale for local methods.
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Asserin, O., Loredo, A., Petelet, M., Iooss, B.: Global sensitivity analysis in welding simulations-What are the material data you really need? Finite Elem.
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