P. Alberto and F. González, Partial Least Squares regression on symmetric positive-definite matrices, Revista Colombiana de Estadística, vol.36, issue.1, pp.177-192, 2012.

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

C. Bishop, B. Jemaa, S. Sayrac, B. Fort, G. Moulines et al., Pattern Recognition and Machine Learning (Information Science and Statistics) Low complexity spatial interpolation for cellular coverage analysis, Modeling and Optimization in Mobile , Ad Hoc, and Wireless Networks (WiOpt), 2014 12th International Symposium on, pp.188-195, 2007.

N. Cressie, Spatial prediction and ordinary kriging, Mathematical Geology, vol.16, issue.4, pp.405-421, 1988.
DOI : 10.1007/BF00892986

A. Damianou and N. Lawrence, Deep gaussian processes, Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013, pp.207-215, 2013.

N. Durrande, Covariance kernels for simplified and interpretable modeling. a functional and probabilistic approach . theses, Ecole Nationale Supérieure des Mines de saint-Etienne Durrande N, Ginsbourger D, Roustant O (2012) Additive covariance kernels for high-dimensional gaussian process modeling, Annales de la faculté des sciences de Toulouse Mathématiques, pp.481-499, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00844747

A. Forrester, A. Sobester, and A. Keane, Engineering Design via Surrogate Modelling: A Practical Guide Wiley Frank IE, Friedman JH (1993) A statistical view of some chemometrics regression tools, Technometrics, vol.35, pp.109-148, 2008.
DOI : 10.1002/9780470770801

P. Goovaerts, Geostatistics for Natural Resources Evaluation (Applied Geostatistics) Neural Networks: A Comprehensive Foundation, 1997.

I. Helland, On the structure of partial least squares regression, Communications in Statistics - Simulation and Computation, vol.5, issue.2, pp.581-607, 1988.
DOI : 10.1137/0905052

J. Hensman, N. Fusi, and N. Lawrence, Gaussian processes for big data, Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013.

D. Jones, M. Schonlau, and W. 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

C. Lanczos, An iteration method for the solution of the eigenvalue problem of linear differential and integral operators, Journal of Research of the National Bureau of Standards, vol.45, issue.4, pp.255-282, 1950.
DOI : 10.6028/jres.045.026

R. Liem and J. Martins, Surrogate models and mixtures of experts in aerodynamic performance prediction for mission analysis, 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, pp.2014-2301, 2014.

R. Manne, Analysis of two partial-least-squares algorithms for multivariate calibration, Chemometrics and Intelligent Laboratory Systems, vol.2, issue.1-3, pp.1-3187, 1987.
DOI : 10.1016/0169-7439(87)80096-5

N. Mera, Efficient optimization processes using kriging approximation models in electrical impedance tomography, International Journal for Numerical Methods in Engineering, vol.13, issue.1, pp.202-220, 2007.
DOI : 10.1002/nme.1772

Z. Michalewicz and M. Schoenauer, Evolutionary Algorithms for Constrained Parameter Optimization Problems, Evolutionary Computation, vol.13, issue.1, pp.1-32, 1996.
DOI : 10.1162/evco.1996.4.1.1

G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., noesissolutions.com/Noesis/optimus- details/optimus-design-optimization Pedregosa F Scikit-learn: Machine learning in python, Noesis Solutions The Journal of Machine Learning Research, vol.12, pp.2825-2830, 2009.

V. Picheny, D. Ginsbourger, O. Roustant, R. Haftka, and N. Kim, Adaptive Designs of Experiments for Accurate Approximation of a Target Region, Journal of Mechanical Design, vol.132, issue.7, p.71008, 2010.
DOI : 10.1115/1.4001873

URL : https://hal.archives-ouvertes.fr/emse-00699752

M. Powell, A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation, pp.51-67, 1994.
DOI : 10.1007/978-94-015-8330-5_4

C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning Adaptive Computation and Machine Learning Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization, Engineering Optimization, vol.45, issue.5, pp.529-555, 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, pp.1-55, 2012.
DOI : 10.18637/jss.v051.i01

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

S. Sakata, F. Ashida, and M. Zako, An efficient algorithm for Kriging approximation and optimization with large-scale sampling data, Computer Methods in Applied Mechanics and Engineering, vol.193, issue.3-5, pp.385-404, 2004.
DOI : 10.1016/j.cma.2003.10.006

M. Sasena, G. Wahba, and P. Craven, Flexibility and efficiency enhancements for constrained global design optimization with Kriging approximations Computer experiments and global optimization Spline models for observational data Smoothing noisy data with spline functions. estimating the correct degree of smoothing by the method of generalized cross-validation, CBMS-NSF Regional Conference Series in Applied Mathematics, pp.377-404, 1978.

D. Zimmerman and K. Homer, A network design criterion for estimating selected attributes of the semivariogram, Environmetrics, vol.33, issue.4, pp.425-441, 1991.
DOI : 10.1002/env.3770020403