M. Abtini, Plans prédictifsà taille fixe et séquentiels pour le krigeage, 2018.

J. Bect, F. Bachoc, and D. Ginsbourger, A supermartingale approach to gaussian process based sequential design of experiments, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01351088

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.22, issue.3, pp.773-793, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00689580

D. Bolin and F. Lindgren, Excursion and contour uncertainty regions for latent gaussian models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.77, issue.1, pp.85-106, 2015.

A. Bonfils, Y. Creff, O. Lepreux, and N. Petit, Closed-loop control of a scr system using a nox sensor cross-sensitive to nh3, IFAC Proceedings Volumes, vol.45, pp.738-743, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01085244

H. Cardot, F. Ferraty, and P. Sarda, Functional linear model, Statistics & Probability Letters, vol.45, issue.1, pp.11-22, 1999.

C. Chevalier, J. Bect, D. Ginsbourger, E. Vazquez, V. Picheny et al., Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set, Technometrics, vol.56, issue.4, pp.455-465, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00641108

C. Chevalier, X. Emery, and D. Ginsbourger, Fast update of conditional simulation ensembles, Mathematical Geosciences, vol.47, issue.7, pp.771-789, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00984515

C. Chevalier and D. Ginsbourger, Fast computation of the multipoints expected improvement with applications in batch selection, International Conference on Learning and Intelligent Optimization, pp.59-69, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00732512

C. Chevalier, D. Ginsbourger, J. Bect, and I. Molchanov, Estimating and quantifying uncertainties on level sets using the vorobev expectation and deviation with gaussian process models, mODa 10-Advances in Model-Oriented Design and Analysis, pp.35-43, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00731783

C. Chevalier, V. Picheny, and D. Ginsbourger, Kriginv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging, Computational statistics & data analysis, vol.71, pp.1021-1034, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00713537

B. A. Flury, Principal points, Biometrika, vol.77, issue.1, pp.33-41, 1990.

J. P. French and S. R. Sain, Spatio-temporal exceedance locations and confidence regions, The Annals of Applied Statistics, vol.7, issue.3, pp.1421-1449, 2013.

D. A. Jackson, Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches, Ecology, vol.74, issue.8, pp.2204-2214, 1993.

J. Janusevskis and R. Le-riche, Simultaneous kriging-based estimation and optimization of mean response, Journal of Global Optimization, vol.55, issue.2, pp.313-336, 2013.
URL : https://hal.archives-ouvertes.fr/emse-00674460

R. Jin, W. Chen, and A. Sudjianto, An efficient algorithm for constructing optimal design of computer experiments, Journal of Statistical Planning and Inference, vol.134, issue.1, pp.268-287, 2005.

M. E. Johnson, L. M. Moore, and D. Ylvisaker, Minimax and maximin distance designs, Journal of statistical planning and inference, vol.26, issue.2, pp.131-148, 1990.

P. Lecuyer and C. Lemieux, Recent advances in randomized quasimonte carlo methods, Modeling uncertainty, pp.419-474, 2005.

C. Levrard, High-dimensional vector quantization: convergence rates and variable selection, vol.11, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01093476

H. Luschgy and G. Pagès, Greedy vector quantization, Journal of Approximation Theory, vol.198, pp.111-131, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01026116

H. Luschgy, G. Pagès, and B. Wilbertz, Asymptotically optimal quantization schemes for gaussian processes on hilbert spaces, ESAIM: Probability and Statistics, vol.14, pp.93-116, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00591695

M. Miranda and P. Bocchini, Functional quantization of stationary gaussian and non-gaussian random processes. Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures pp, pp.2785-2792, 2013.

M. J. Miranda and P. Bocchini, A versatile technique for the optimal approximation of random processes by functional quantization, Applied Mathematics and Computation, vol.271, pp.935-958, 2015.

M. D. Morris and T. J. Mitchell, Exploratory designs for computational experiments, Journal of statistical planning and inference, vol.43, issue.3, pp.381-402, 1995.

S. Nanty, C. Helbert, A. Marrel, N. Pérot, and C. Prieur, Sampling, metamodeling, and sensitivity analysis of numerical simulators with functional stochastic inputs, SIAM/ASA Journal on Uncertainty Quantification, vol.4, issue.1, pp.636-659, 2016.

G. Pagès, Introduction to optimal vector quantization and its applications for numerics, 2014.

G. Pagès and J. Printems, Functional quantization for numerics with an application to option pricing, Monte Carlo Methods and Applications mcma, vol.11, issue.4, pp.407-446, 2005.

G. Pages and J. Printems, Optimal quantization for finance: from random vectors to stochastic processes, Handbook of Numerical Analysis, vol.15, pp.595-648, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00392177

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.132, issue.7, p.71008, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00319385

L. Pronzato and W. G. Müller, Design of computer experiments: space filling and beyond, Statistics and Computing, vol.22, issue.3, pp.681-701, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00685876

J. O. Ramsay, Functional data analysis, 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, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00495766

E. Vazquez and J. Bect, A sequential bayesian algorithm to estimate a probability of failure, IFAC Proceedings Volumes, vol.42, pp.546-550, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00368158

B. J. Williams, T. J. Santner, and W. I. Notz, Sequential design of computer experiments to minimize integrated response functions. Statistica Sinica pp, pp.1133-1152, 2000.