F. Bachoc, Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00881002

C. T. Baker, The Numerical Treatment of Integral Equations, Journal of Applied Mechanics, vol.46, issue.4, 1977.
DOI : 10.1115/1.3424708

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

J. O. Berger, V. D. Oliveira, and B. Sansó, Objective Bayesian Analysis of Spatially Correlated Data, Journal of the American Statistical Association, vol.96, issue.456, pp.1361-1374, 2001.
DOI : 10.1198/016214501753382282

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.2459-2468, 2008.
DOI : 10.1109/JMEMS.2004.825308

C. Chevalier, J. Bect, D. Ginsbourger, and E. Vazquez, Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set, Technometrics, vol.13, issue.4, pp.455-465, 2014.
DOI : 10.1007/3-540-50871-6

URL : https://hal.archives-ouvertes.fr/hal-00641108

B. Echard, N. Gayton, and M. Lemaire, AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation, Structural Safety, vol.33, issue.2, pp.145-154, 2011.
DOI : 10.1016/j.strusafe.2011.01.002

K. T. Fang, R. Li, and A. Sudjianto, Design and modeling for computer experiments, Computer Science and Data Analysis Series, vol.8, 2006.
DOI : 10.1201/9781420034899

K. T. Fang and D. K. Lin, Ch. 4. Uniform experimental designs and their applications in industry, Handbook of Statistics, vol.22, pp.131-178, 2003.
DOI : 10.1016/S0169-7161(03)22006-X

URL : https://hal.archives-ouvertes.fr/hal-01541536

D. Ginsbourger, R. Le-riche, and L. Carraro, Computational Intelligence in Expensive Optimization Problems, volume 2 of Adaptation Learning and Optimization , chapter Kriging Is Well-Suited to Parallelize Optimization, pp.131-162, 2010.

R. Gramacy and H. Lian, Gaussian Process Single-Index Models as Emulators for Computer Experiments, Technometrics, vol.35, issue.6, pp.30-41, 2012.
DOI : 10.1016/j.csda.2008.12.010

URL : http://arxiv.org/pdf/1009.4241

R. B. Gramacy and H. K. Lee, Cases for the nugget in modeling computer experiments, Statistics and Computing, vol.4, issue.4, pp.713-722, 2012.
DOI : 10.1007/978-1-4612-1494-6

R. Hu and M. Ludkovski, Sequential Design for Ranking Response Surfaces, SIAM/ASA Journal on Uncertainty Quantification, vol.5, issue.1, pp.212-239, 2017.
DOI : 10.1137/15M1045168

M. C. Kennedy and A. O. Hagan, Predicting the output from a complex computer code when fast approximations are available, Biometrika, vol.87, issue.1, pp.1-13, 2000.
DOI : 10.1093/biomet/87.1.1

M. C. Kennedy and A. O. Hagan, Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.3, pp.425-464, 2001.
DOI : 10.1111/1467-9868.00294

P. C. Jack and . Kleijnen, Regression and kriging metamodels with their experimental designs in simulation: A review, European Journal of Operational Research, vol.256, pp.1-16, 2017.

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

R. Paulo, Default priors for Gaussian processes, The Annals of Statistics, vol.33, issue.2, pp.556-582, 2005.
DOI : 10.1214/009053604000001264

URL : http://doi.org/10.1214/009053604000001264

G. Perrin, Active learning surrogate models for the conception of systems with multiple failure modes, Reliability Engineering & System Safety, vol.149, pp.130-136, 2016.
DOI : 10.1016/j.ress.2015.12.017

URL : https://hal.archives-ouvertes.fr/hal-01266534

G. Perrin and C. Cannamela, A repulsion-based method for the definition and the enrichment of opotimized space filling designs in constrained input spaces, Journal de la Société Française de Statistique, vol.158, issue.1, pp.37-67, 2017.

G. Perrin, C. Soize, S. Marque-pucheu, and J. Garnier, Nested polynomial trends for the improvement of Gaussian process-based predictors, Journal of Computational Physics, vol.346, pp.389-402, 2017.
DOI : 10.1016/j.jcp.2017.05.051

URL : https://hal.archives-ouvertes.fr/hal-01298861

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

C. Robert, The Bayesian Choice, 2007.
DOI : 10.1007/978-1-4757-4314-2

J. Sacks, W. Welch, T. J. Mitchell, and H. P. Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-435, 1989.
DOI : 10.1214/ss/1177012413

T. J. Santner, B. J. Williams, and W. Notz, The design and analysis of computer experiments. Springer series in statistics, 2003.