A. Antoniadis, C. Helbert, C. Prieur, and L. Viry, Spatio-temporal metamodeling for West African monsoon, Environmetrics, vol.33, issue.6, pp.24-36, 2012.
DOI : 10.1002/env.1134

URL : https://hal.archives-ouvertes.fr/hal-00551303

M. Binois, D. Ginsbourger, R. , and O. , Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations, European Journal of Operational Research, vol.243, issue.2, pp.386-394, 2015.
DOI : 10.1016/j.ejor.2014.07.032

URL : https://hal.archives-ouvertes.fr/hal-00904811

A. J. Booker, J. Dennis, J. E. Frank, P. D. Serafini, D. B. Torczon et al., Optimization Using Surrogate Objectives on a Helicopter Test Example, Computational methods for optimal design and control of Progr. Systems Control Theory, pp.49-58, 1997.
DOI : 10.1007/978-1-4612-1780-0_3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.1634

D. Cornford, I. T. Nabney, W. , and C. K. , Modelling Frontal Discontinuities in Wind Fields, Statistical models and methods for discontinuous phenomena, pp.43-58, 1998.
DOI : 10.1029/96JC02860

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.634.4846

N. A. Cressie, Statistics for spatial data Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics, 1993.

N. Durrande, Étude de classes de noyaux adaptées à la simplification et à l'interprétation des modèles d'approximation. Une approche fonctionnelle et probabiliste, 2001.

N. Durrande, D. Ginsbourger, R. , and O. , Additive Covariance kernels for high-dimensional Gaussian Process modeling, Annales de la facult? des sciences de Toulouse Math?matiques, vol.21, issue.3, pp.481-499, 2012.
DOI : 10.5802/afst.1342

URL : https://hal.archives-ouvertes.fr/hal-00644934

N. Durrande, D. Ginsbourger, O. Roustant, C. , and L. , ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis, Journal of Multivariate Analysis, vol.115, pp.57-67, 2013.
DOI : 10.1016/j.jmva.2012.08.016

URL : https://hal.archives-ouvertes.fr/hal-00601472

B. S. Everitt, S. Landau, M. Leese, and D. Stahl, Cluster analysis. Wiley Series in Probability and Statistics, 2011.
DOI : 10.1002/9780470977811

T. E. Fricker, J. E. Oakley, and N. M. Urban, Multivariate Gaussian Process Emulators With Nonseparable Covariance Structures, Technometrics, vol.69, issue.1, pp.47-56, 2013.
DOI : 10.1080/00401706.2012.715835

J. Gao, S. Gunn, and J. Kandola, Adapting Kernels by Variational Approach in SVM, AI 2002: Advances in artificial intelligence, pp.395-406, 2002.
DOI : 10.1007/3-540-36187-1_35

D. Ginsbourger, O. Roustant, D. Schuhmacher, N. Durrande, and N. Lenz, On ANOVA decompositions of kernels and Gaussian random field paths. In Monte Carlo and quasi-Monte Carlo methods, Math. Stat, vol.163, pp.315-330, 2016.
URL : https://hal.archives-ouvertes.fr/emse-01339368

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Computational Statistics & Data Analysis, vol.52, issue.10, pp.524731-4744, 2008.
DOI : 10.1016/j.csda.2008.03.026

URL : https://hal.archives-ouvertes.fr/hal-00239492

T. Muehlenstaedt, O. Roustant, L. Carraro, and S. Kuhnt, Data-driven Kriging models based on FANOVA-decomposition, Statistics and Computing, vol.34, issue.4, pp.723-738, 2012.
DOI : 10.1007/s11222-011-9259-7

URL : https://hal.archives-ouvertes.fr/emse-00699673

C. J. Paciorek and M. J. Schervish, Spatial modelling using a new class of nonstationary covariance functions, Environmetrics, vol.99, issue.5, pp.483-506, 2006.
DOI : 10.1002/env.785

E. Padonou and O. Roustant, Polar Gaussian Processes and Experimental Designs in Circular Domains, SIAM/ASA Journal on Uncertainty Quantification, vol.4, issue.1, pp.1014-1033, 2016.
DOI : 10.1137/15M1032740

URL : https://hal.archives-ouvertes.fr/emse-01412189

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, Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization, Journal of Statistical Software, vol.51, issue.1, 2012.
DOI : 10.18637/jss.v051.i01

URL : https://hal.archives-ouvertes.fr/hal-00495766

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

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

E. L. Snelson, Flexible and efficient Gaussian process models for machine learning, 2008.

M. L. Stein, Interpolation of spatial data. Springer Series in Statistics, 1999.

M. Stitson, A. Gammerman, V. Vapnik, V. Vovk, C. Watkins et al., Support vector regression with anova decomposition kernels Advances in kernel methodsSupport vector learning, pp.285-292, 1997.

B. Sudret, Meta-models for structural reliability and uncertainty quantification. arXiv preprint arXiv:1203, 2012.
DOI : 10.3850/978-981-07-2219-7_p321

URL : https://hal.archives-ouvertes.fr/hal-00683179

N. Villa-vialaneix, M. Follador, M. Ratto, and A. Leip, A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops, Environmental Modelling & Software, vol.34, pp.51-66, 2012.
DOI : 10.1016/j.envsoft.2011.05.003

URL : https://hal.archives-ouvertes.fr/hal-00654753

W. J. Welch, R. J. Buck, J. Sacks, H. P. Wynn, T. J. Mitchell et al., Screening, Predicting, and Computer Experiments, Technometrics, vol.34, issue.1, pp.15-25, 1992.
DOI : 10.2307/1269548

G. Yi, Variable selection with penalized Gaussian process regression models, 2009.