E. De-rocquigny, N. Devictor, and S. Tarantola, Uncertainty in industrial practice: a guide to quantitative uncertainty management, 2008.
DOI : 10.1002/9780470770733

E. Sergienko and D. Busby, Optimal Well Placement for Risk Mitigation in CO2 Storage, 1st Sustainable Earth Sciences Conference and Exhibition (SES2011), 2011.
DOI : 10.3997/2214-4609.20144195

D. Busby and E. Sergienko, Combining Probabilistic Inversion and Multi-objective Optimization for Production Development under Uncertainty, 12th European Conference on the Mathematics of Oil Recovery, 2010.
DOI : 10.3997/2214-4609.20144979

S. Subbey, M. Christie, and M. Sambridge, Prediction under uncertainty in reservoir modeling, Journal of Petroleum Science and Engineering, vol.44, issue.1-2, pp.143-153, 2004.
DOI : 10.1016/j.petrol.2004.02.011

D. Busby and M. Feraille, Adaptive design of experiments for bayesian inversion an application to uncertainty quantification of a mature oil field, Journal of Physics Conference Series, vol.135

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

M. D. Mckay, R. J. Beckman, and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, pp.239-245, 1979.

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

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

G. Matheron, Principles of geostatistics, Economic Geology, vol.58, issue.8, pp.1246-1266, 1963.
DOI : 10.2113/gsecongeo.58.8.1246

S. Conti and A. Ohagan, Bayesian emulation of complex multi-output and dynamic computer models, Journal of Statistical Planning and Inference, vol.140, issue.3, pp.640-651, 2010.
DOI : 10.1016/j.jspi.2009.08.006

P. Z. Qian, H. Wu, and C. J. Wu, Gaussian Process Models for Computer Experiments With Qualitative and Quantitative Factors, Technometrics, vol.50, issue.3
DOI : 10.1198/004017008000000262

S. Conti, J. P. Gosling, J. Oakley, and A. O. Hagan, Gaussian process emulation of dynamic computer codes, Biometrika, vol.96, issue.3, pp.663-676, 2009.
DOI : 10.1093/biomet/asp028

M. Bayarri, J. Berger, J. Cafeo, G. Garcia-donato, F. Liu et al., Computer model validation with functional output, The Annals of Statistics, pp.1874-1906, 2007.

K. Campbell, M. D. Mckay, and B. J. Williams, Sensitivity analysis when model outputs are functions, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1468-1472, 2006.
DOI : 10.1016/j.ress.2005.11.049

D. Higdon, J. Gattiker, B. Williams, and M. Rightley, Computer Model Calibration Using High-Dimensional Output, Journal of the American Statistical Association, vol.103, issue.482
DOI : 10.1198/016214507000000888

M. Lamboni, D. Makowski, S. Lehuger, B. Gabrielle, and H. Monod, Multivariate global sensitivity analysis for dynamic crop models, Field Crops Research, vol.113, issue.3, pp.312-320, 2009.
DOI : 10.1016/j.fcr.2009.06.007
URL : https://hal.archives-ouvertes.fr/hal-01173193

B. Auder, A. De-crecy, B. Iooss, and M. Marques, Screening and metamodeling of computer experiments with functional outputs. Application to thermal???hydraulic computations, Reliability Engineering & System Safety, vol.107, pp.122-131, 2012.
DOI : 10.1016/j.ress.2011.10.017
URL : https://hal.archives-ouvertes.fr/hal-00525491

W. Lawton, E. Sylvestre, and M. Maggio, Self Modeling Nonlinear Regression, Technometrics, vol.2, issue.3, pp.513-532, 1972.
DOI : 10.1080/01621459.1969.10501038

L. C. Brumback and M. J. Lindstrom, Self Modeling with Flexible, Random Time Transformations, Biometrics, vol.44, issue.2, 2004.
DOI : 10.1111/j.0006-341X.2004.00191.x

R. Izem and J. Marron, Analysis of nonlinear modes of variation for functional data, Electronic Journal of Statistics, vol.1, issue.0, pp.641-676, 2007.
DOI : 10.1214/07-EJS080

D. G. Krige, A statistical approach to some basic mine valuation problems on the Witwatersrand, Journal of the Chemical, Metallurgical and Mining Society of South Africa, vol.52, issue.6, p.119139, 1951.

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

A. I. Forrester and A. J. Keane, Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, vol.45, issue.1-3, pp.50-79, 2009.
DOI : 10.1016/j.paerosci.2008.11.001

M. Vimond, Efficient estimation for a subclass of shape invariant models, The Annals of Statistics, vol.38, issue.3, pp.1885-1912, 2010.
DOI : 10.1214/07-AOS566
URL : https://hal.archives-ouvertes.fr/hal-00537840

F. Gamboa, J. Loubes, and E. Maza, Semi-parametric estimation of shifts, Electronic Journal of Statistics, vol.1, issue.0, pp.616-640, 2007.
DOI : 10.1214/07-EJS026
URL : https://hal.archives-ouvertes.fr/hal-00347905