, These systematic errors are the same for the three metamodels, so that

H. Golfier, R. Lenain, C. Calvin, J. J. Lautard, A. M. Baudron et al.,

P. H. Fougeras, E. Magat, Y. Martinolli, and . Dutheillet, APOLLO3: a common project of CEA, AREVA and EDF for the development of a new deterministic multi-purpose code for core physics analysis, Int Conf. on Math., Computational Meth., M&C2009, 2009.

G. Geffraye, O. Antoni, M. Farvacque, D. Kadri, G. Lavialle et al., CATHARE 2 V2.5 2: A single version for various applications, Nuclear Engineering and Design, vol.241, issue.11, pp.4456-4463, 2011.

E. Hourcade, X. Ingremeau, P. Dumaz, S. Dardour, D. Schmitt et al., Innovative methodologies for fast reactor core design and optimization, ICAPP Nice, 2011.

L. Roche and M. Pelletier, Modelling of the thermomechanical and physical processes in FR fuel pins using the GERMINAL code, MOX Fuel Cycle Technologies for Medium and Long Term Deployment, p.322, 2000.

E. Hourcade, F. Jasserand, K. Ammar, and C. Patricot, SFR core design a system-driven multi-criteria core optimisation exercice with TRIAD, 2013.

D. G. Cacuci, Sensitivity and uncertainty analysis, 2003.

D. G. Cacuci and M. Ionescu-bujor, Best-Estimate model calibration and prediction through experimental data assimilation-I: Mathematical framework, Nuclear Science and Engineering, vol.165, pp.18-44, 2010.

D. Higdon, M. Kennedy, J. C. Cavendish, J. A. Cafeo, and R. D. Ryne, Combining field data and computer simulations for calibration and prediction, SIAM Journal on Scientific Computing, vol.26, pp.448-466, 2004.

J. C. Le-pallec, C. Poinot-salanon, N. Crouzet, and S. Zimmer, HEMERA V2: An evolutionary tool for PWR multi-physics analysis in salome platform, Proceedings of ICAPP 2011, p.2851, 2011.

T. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.

B. A. Lockwood and M. Anitescu, Gradient-enhanced universal Kriging for uncertainty propagation, Nuclear Science and Engineering, vol.170, pp.168-195, 2012.

F. Bachoc, G. Bois, J. Garnier, and J. M. Martinez, Calibration and improved prediction of computer models by universal Kriging, Nuclear Science and Engineering, vol.176, issue.1, pp.81-97, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01020594

M. Stein, Interpolation of Spatial Data: Some Theory for Kriging, 1999.

C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, 2006.

I. Andrianakis and P. G. Challenor, The effect of the nugget on Gaussian process emulators of computer models, Computational Statistics and Data Analysis, vol.56, pp.4215-4228, 2012.

F. Bachoc, Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model mispecification, Computational Statistics and Data Analysis, vol.66, pp.55-69, 2013.

F. Bachoc, Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes, Journal of Multivariate Analysis, vol.125, pp.1-35, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00906934

O. Dubrule, Cross validation of Kriging in a unique neighborhood, Mathematical Geology, vol.15, pp.687-699, 1983.

G. Wahba, Spline Models for Observational Data, Society for Industrial and Applied Mathematics, 1990.

B. Schölkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization and beyond, 2002.

G. Golub, M. Heath, and G. Wahba, Generalized cross-validation as a method for choosing a good ridge parameter, Technometrics, vol.21, issue.2, pp.215-223, 1979.

R. X. Yue and F. J. Hickernell, Robust designs for fitting linear models with misspecification, Statistica Sinica, vol.9, pp.1053-1069, 1999.

G. Dreyfus, Neural Networks, Methodology and Applications, 2005.

F. Gaudier, URANIE: The CEA DEN uncertainty and sensitivity platform, Procedia -Social and Behavioral Sciences, vol.2, pp.7660-7661, 2010.

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal representation by error backpropagation, Parallel Distributed Processing : Explorations in the Microstructures of Cognition, pp.318-362, 1986.

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Computational Statistics and Data Analysis, vol.52, pp.4731-4744, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00239492

S. Tufféry, Data Mining and Statistics for Decision Making Methods, 2011.