P. Abrahamsen, A review of Gaussian random fields and correlation functions, Norsk Regnesentral/Norwegian Computing Center, 1997.

F. Bachoc, Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification, Computational Statistics & Data Analysis, vol.66, pp.55-69, 2013.
DOI : 10.1016/j.csda.2013.03.016

F. Bachoc, Estimation paramétrique de la fonction de covariance dans le modèle de Krigeage par processus Gaussiens: application à la quantification des incertitues en simulation numérique, 2013.

F. Bachoc, Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes, Journal of Multivariate Analysis, vol.125, pp.1-35, 2014.
DOI : 10.1016/j.jmva.2013.11.015

URL : https://hal.archives-ouvertes.fr/hal-00906934

F. Bachoc, A. Lagnoux, and T. M. Nguyen, Cross-validation estimation of covariance parameters under fixed-domain asymptotics, Journal of Multivariate Analysis, vol.160, 2017.
DOI : 10.1016/j.jmva.2017.06.003

URL : https://hal.archives-ouvertes.fr/hal-01377854

J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.34, issue.4, pp.773-793, 2012.
DOI : 10.2307/1269548

URL : https://hal.archives-ouvertes.fr/hal-00689580

R. Benassi, J. Bect, and E. Vazquez, Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion, pp.176-190, 2011.
DOI : 10.1002/qre.945

URL : https://hal.archives-ouvertes.fr/hal-00607816

B. J. Bichon, M. S. Eldred, L. P. Swiler, S. Mahadevan, and J. M. Mcfarland, Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions, AIAA Journal, vol.26, issue.2, pp.46-2459, 2008.
DOI : 10.1109/JMEMS.2004.825308

M. Binois, D. Ginsbourger, and O. Roustant, A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings, pp.281-286
DOI : 10.1007/978-3-319-19084-6_28

URL : https://hal.archives-ouvertes.fr/hal-01078003

D. Busby, C. L. Farmer, and A. Iske, Hierarchical Nonlinear Approximation for Experimental Design and Statistical Data Fitting, SIAM Journal on Scientific Computing, vol.29, issue.1, pp.49-69, 2007.
DOI : 10.1137/050639983

B. Chen, A. Krause, and R. M. Castro, Joint optimization and variable selection of high-dimensional Gaussian processes, Proceedings of the 29th International Conference on Machine Learning (ICML- 12), pp.2012-1423

J. H. De-baar, R. P. Dwight, and H. Bijl, Speeding up Kriging through fast estimation of the hyperparameters in the frequency-domain, Computers & Geosciences, vol.54, pp.99-106, 2013.
DOI : 10.1016/j.cageo.2013.01.016

O. Dubrule, Cross validation of kriging in a unique neighborhood, Journal of the International Association for Mathematical Geology, vol.15, issue.6, pp.687-699, 1983.
DOI : 10.1007/BF01033232

A. I. Forrester and D. R. Jones, Global Optimization of Deceptive Functions with Sparse Sampling, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2008.
DOI : 10.1115/1.2829873

F. Hutter, H. H. Hoos, and K. Leyton-brown, Sequential Model-Based Optimization for General Algorithm Configuration, pp.507-523, 2011.
DOI : 10.1007/978-3-642-25566-3_47

URL : http://www.cs.ubc.ca/spider/hutter/papers/10-TR-SMAC.pdf

B. Iooss and P. Lemaître, A review on global sensitivity analysis methods, in Uncertainty Management in Simulation-Optimization of Complex Systems, pp.101-122, 2015.

D. J. Jones, A taxonomy of global optimization methods based on response surfaces, Journal of Global Optimization, vol.21, issue.4, pp.345-383, 2001.
DOI : 10.1023/A:1012771025575

D. J. Jones, M. Schonlau, and W. J. Welch, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.
DOI : 10.1023/A:1008306431147

J. Mockus, The Bayesian approach to global optimization, System Modeling and Optimization, pp.473-481, 1982.

M. D. Morris, T. J. Mitchell, and D. Ylvisaker, Bayesian design and analysis of computer experiments: use of derivatives in surface prediction, Technometrics, pp.35-243, 1993.

V. Picheny, D. Ginsbourger, O. Roustant, R. T. Haftka, and N. H. Kim, Adaptive Designs of Experiments for Accurate Approximation of a Target Region, Journal of Mechanical Design, vol.16, issue.7, pp.71008-071008, 2010.
DOI : 10.1007/978-3-540-30217-9_29

URL : https://hal.archives-ouvertes.fr/hal-00319385

P. Ranjan, D. Bingham, and G. Michailidis, Sequential Experiment Design for Contour Estimation From Complex Computer Codes, Technometrics, vol.50, issue.4, p.50, 2008.
DOI : 10.1198/004017008000000541

C. E. Rasmussen and C. K. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

URL : http://mlg.eng.cam.ac.uk/pub/pdf/Ras04.pdf

O. Roustant, J. Fruth, B. Iooss, and S. Kuhnt, Crossed-derivative based sensitivity measures for interaction screening, Mathematics and Computers in Simulation, vol.105, pp.105-118, 2014.
DOI : 10.1016/j.matcom.2014.05.005

URL : https://hal.archives-ouvertes.fr/hal-00845446

O. Roustant, D. Ginsbourger, and Y. Deville, Dicekriging, diceoptim: Two r packages for the analysis of computer experiments by kriging-based metamodelling and optimization, Journal of Statistical Software, pp.51-54, 2012.
URL : https://hal.archives-ouvertes.fr/emse-00741762

T. J. Santner, B. J. Williams, and W. I. Notz, The Design and Analysis of Computer Experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

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

I. M. and A. L. Gershman, On an alternative global sensitivity estimator, Proceedings of SAMO 1995, pp.40-42, 1995.

I. M. and S. Kucherenko, Derivative-Based Global Sensitivity Measures and Their Link with Sobol??? Sensitivity Indices, Mathematics and Computers in Simulation, vol.181, issue.7, pp.3009-3017, 2009.
DOI : 10.1016/j.cpc.2010.03.006

URL : http://arxiv.org/pdf/1605.07830

M. L. Stein, Interpolation of Spatial Data: Some Theory for Kriging, 1999.
DOI : 10.1007/978-1-4612-1494-6

S. Streltsov and P. Vakili, A non-myopic utility function for statistical global optimization algorithms, Journal of Global Optimization, vol.14, issue.3, pp.283-298, 1999.
DOI : 10.1023/A:1008284229931

Z. Wang, F. Hutter, M. Zoghi, D. Matheson, and N. De-feitas, Bayesian optimization in a billion dimensions via random embeddings, Journal of Artificial Intelligence Research, vol.55, pp.361-387, 2016.

H. Wendland, Scattered data approximation, 2004.
DOI : 10.1017/CBO9780511617539

W. Xu and M. L. Stein, Maximum Likelihood Estimation for a Smooth Gaussian Random Field Model, SIAM/ASA Journal on Uncertainty Quantification, vol.5, issue.1, pp.138-175, 2017.
DOI : 10.1137/15M105358X