M. A. Aizerman, E. A. Braverman, and L. Rozonoer, Theoretical foundations of the potential function method in pattern recognition learning, pp.821-837, 1964.

A. M. Anoop and A. Messac, Metamodeling using extended radial basis functions: a comparative approach, Engineering with Computers, vol.21, pp.203-217, 2006.

S. Asmussen and P. W. Glynn, Stochastic simulation: algorithms and analysis, 2007.

S. Au and J. L. Beck, Estimation of small failure probabilities in high dimensions by subset simulation, Prob. Eng. Mech, vol.16, pp.263-277, 2001.

F. Bachoc, Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecifications, Comput. Stat. Data Anal, vol.66, pp.55-69, 2013.

M. Balesdent, J. Morio, and J. Marzat, Kriging-based adaptive importance sampling algorithms for rare event estimation, Structural Safety, vol.44, pp.1-10, 2013.

G. Blatman and B. Sudret, Adaptive sparse polynomial chaos expansions-application to structural reliability, Proc. 4th Int. ASRANet Colloquium, 2008.

G. Blatman and B. Sudret, An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis, Prob. Eng. Mech, vol.25, pp.183-197, 2010.

G. Blatman and B. Sudret, Reliability analysis of a pressurized water reactor vessel using sparse polynomial chaos expansions, 2010.

, Proc. 15th IFIP WG7.5 Conference on Reliability and Optimization of Structural Systems, pp.9-16

M. Bompard, Modèles de substitution pour l'optimisation globale de forme en aérodynamique et méthode locale sans paramétrisation, 2011.

J. Bourinet, Reliability assessment with adaptive surrogates based on support vec tor machine regression, Proc. 1st ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, 2015.

J. Bourinet, F. Deheeger, and M. Lemaire, Assessing small failure probabilities by combined subset simulation and support vector machines, Structural Safety, vol.33, issue.6, pp.343-353, 2011.

G. E. Box and N. R. Draper, Empirical model building and response surface, 1986.

D. Broomhead and D. Lowe, Multivariable functional interpolation and adaptive networks, AIP Conf. Proc, vol.2, pp.321-355, 1988.

M. Chang and C. Lin, Leave-one-out bounds for support vector regression model selection, Neural Comput, vol.17, issue.5, pp.1188-1222, 2005.

O. Chapelle, Support Vector Machines : principes d'induction, réglage automatique et connaissances a priori, 2002.

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherje, Choosing multiple parameters for support vector machines, Mach. Learn, vol.46, pp.131-159, 2002.

V. Cherkassky and F. Mulier, Learning from data: concepts, theory, and methods, 2007.

C. Cortes and V. Vapnik, Support vector networks, Mach. Learn, pp.273-297, 1995.

E. De-rocquigny, Modelling under risk and uncertainty-An introduction to statistical, phenomenological and computational methods. Wiley series in probability and statistics, 2012.

F. Deheeger and M. Lemaire, Support vector machine for efficient subset simulations: 2 SMART method, Proc. 10th Int. Conf. on Applications of Stat. and Prob. in Civil Engineering (ICASP10), 2007.

Q. Du, V. Faber, and M. Gunzburger, Centroidal Voronoi tessellations: applications and algorithms, SIAM Rev, vol.41, issue.4, pp.637-676, 1999.

V. Dubourg, Adaptive surrogate models for reliability analysis and reliability-based design optimization, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00697026

V. Dubourg, B. Sudret, and J. Bourinet, Reliability-based design optimization using Kriging and subset simulation, Struct. Multidisc. Optim, vol.44, issue.5, pp.673-690, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00587311

H. Fang, M. Rais-rohani, Z. Liu, and M. F. Horstemeyer, A comparative study of metamodeling method for multiobjective crashworthiness optimization, Comput. Struct, vol.83, pp.2121-2136, 2005.

J. Franco, Planification d'expériences numériques en phase exploratoire pour la simulation des phénomènes complexes, 2008.

R. G. Ghanem and P. D. Spanos, Stochastic finite elements: a spectral approach, 2003.

D. Ginsbourger, Multiples métamodèles pour l'approximation et l'optimisation de fonctions numériques multivariables, 2009.

S. Gunn, Support vector machines for classification and regression, 1998.

N. Hansen, The CMA Evolution Strategy: A tutorial, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01297037

N. Hansen and S. Kern, Evaluating the CMA-evolution strategy on multimodal test functions. In Parallel Problem Solving from Nature-PPSN VIII, Lecture Notes in Computer Science, vol.3242, pp.282-291, 2004.

N. Hansen and A. Ostermeier, Completely derandomized self-adaptation in evolution strategies, Evolutionary Computation, vol.9, issue.2, pp.159-195, 2001.

J. E. Hurtado, Filtered importance sampling with support vector margin: A powerful method for structural reliability analysis, Structural Safety, vol.29, issue.1, pp.2-15, 2007.

R. Jin, W. Chen, and T. W. Simpson, Comparative studies of metamodeling techniques under multiple modeling criteria, Struct. Multidiscip. O, vol.23, pp.1-13, 2000.

D. R. Jones, A taxonomy of global optimization methods based on response surfaces, J. Global Optim, vol.21, issue.4, pp.345-383, 2001.

D. R. Jones, M. Schonlau, and W. J. Welch, Efficient global optimization of expensive black-box functions, J. Global Optim, vol.13, issue.4, pp.455-492, 1998.

J. P. Kleijnen, Design and Analysis of Simulation Experiments, 2007.

J. R. Koehler and A. Owen, Computer experiments, Handbook of Statistics, vol.13, pp.261-308, 1996.

D. G. Krige, A statistical approach to some basic mine valuation problems on the Witwatersrand, J. Chem., Metal. and Mining Soc. of South Africa, vol.52, issue.6, pp.119-139, 1951.

Y. F. Li, S. H. Ng, M. Xie, and T. N. Goh, A systematic comparison of metamodeling techniques for simulation optimization in decision support systems, Appl. Soft Comput, vol.10, issue.4, pp.1257-1273, 2010.

P. Liu and A. Der-kiureghian, Optimization algorithms for structural reliability, Structural Safety, vol.9, pp.161-177, 1991.

S. N. Lophaven, H. B. Nielsen, and J. Sondergaard, Aspects of the Matlab toolbox DACE, 2002.

S. N. Lophaven, H. B. Nielsen, and J. Sondergaard, DACE-A Matlab Kriging toolbox-Version 2.0. Technical University Denmark, 2002.

S. Marelli and B. Sudret, UQLab: A framework for uncertainty quantification in Matlab, Vulnerability, Uncertainty and Risk, Proc. 2nd Int. Conf. on Vulnerability, Risk and analysis management (ICVRAM2014), pp.2554-2563, 2014.

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Comput. Stat. Data Anal, vol.52, pp.4731-4744, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00239492

G. Matheron, La théorie des variables régionalisées et ses applications, 1971.

W. S. Mcculloch and W. Pitts, Neurocomputing: foundations of research. Chapter A logical calculus of the ideas immanent in nervous activity, pp.15-27, 1988.

M. D. Mckay, R. J. Beckman, and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.42, issue.1, pp.55-61, 1979.

M. A. Nik, K. Fayazbakhsh, D. Pasini, and L. Lessard, A comparative study of metamodelling methods for the design optimization of variable stiffness composites, Composite Structures, vol.107, pp.494-501, 2014.

A. O'hagan, Bayesian analysis of computer code outputs: A tutorial, Reliab. Eng. Syst. Safe, vol.91, pp.1290-1300, 2006.

V. Picheny, D. Ginsbourger, O. Roustant, R. T. Haftka, and N. H. Kim, Adaptive designs of experiments for accurate approximation of a target region, J. Mech. Design, vol.132, issue.7, 2010.
URL : https://hal.archives-ouvertes.fr/emse-00699752

C. E. Rasmussen and C. K. Williams, Gaussian processes for machine learning (adaptive computation and machine learning), 2006.

O. Roustant, D. Ginsbourger, and Y. Deville, DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by Kriging-based metamodeling and optimization, J. Stat. Software, vol.51, issue.1, pp.1-55, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00495766

W. Roux, N. Stander, F. Günther, and H. Müllerschön, Stochastic analysis of highly non-linear structures, Int. J. Numer. Meth. Eng, vol.65, pp.1221-1241, 2006.

J. Sacks, W. Welch, T. Mitchell, and H. Wynn, Design and analysis of computer experiments, Stat. Sci, vol.4, issue.4, pp.409-423, 1989.

T. Santner, B. Williams, and W. Notz, The design and analysis of computer experiments, 2003.

M. J. Sasena, Flexibility and efficiency enhancements for constrained global design optimization with Kriging approximations, 2002.

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Stat. Comp, vol.14, issue.3, pp.199-222, 2004.

I. M. Sobol, On the distribution of points in a cube and the approximate evaluation of integrals, USSR Comp. Math, vol.16, pp.784-802, 1967.

B. Sudret, Uncertainty propagation and sensitivity analysis in mechanical modelsContributions to structural reliability and stochastic spectral methods, 2007.

C. Thole and L. Mei, Reasons for scatter in crash simulations results, 4th European LS-DYNA Users Conference, 2003.

V. Vapnik, The nature of statistical learning theory, 1995.

V. Vapnik, Statistical learning theory, 1998.

V. Vapnik and O. Chapelle, Bounds on error expectation for support vector machines, Neural Comput, vol.12, issue.9, pp.2013-2036, 2000.

E. Vazquez, Modélisation comportementale de systèmes non-linéaires multivariables par méthodes à noyaux et applications, 2005.

D. Wei and S. Rahman, Stuctural reliability analysis by univariate decomposition and numerical integration, Prob. Eng. Mech, vol.22, pp.27-38, 2007.
DOI : 10.1016/j.probengmech.2006.05.004

C. Yeh, C. Huang, and S. Lee, A multiple-kernel support vector regression approach for stock market price forecasting, Expert Systems with Applications, vol.38, issue.3, pp.2177-2186, 2011.
DOI : 10.1016/j.eswa.2010.08.004

D. Zhao and D. Xue, A comparative study of metamodeling methods considering sample quality merits, Struct. Multidiscip. O, vol.42, pp.923-938, 2010.
DOI : 10.1007/s00158-010-0529-3