J. Baccou, J. Zhang, P. Fillion, G. Damblin, A. Petruzzi et al., Development of good practice guidance for quantification of thermal-hydraulic code model input uncertainty, Nuclear Engineering and Design, vol.354, p.110173, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02530001

V. Chabridon, A. Marrel, and B. Iooss, Tools for global and target sensitivity analyses in the context of high-dimensional thermal-hydraulic numerical experiments, BEPU 2020 -Preprint HAL-02877385, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02877385

C. Charignon, J. Lecoy, and J. Sauvage, CathSBI, A new methodology for the revised French LOCA rules, NUTHOS-11, 2016.

S. D. Veiga, Global sensitivity analysis with dependence measures, Journal of Statistical Computation and Simulation, vol.85, pp.1283-1305, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00903283

M. De-lozzo and A. Marrel, New improvements in the use of dependence measures for sensitivity analysis and screening, Journal of Statistical Computation and Simulation, vol.86, pp.3038-3058, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01090475

K. Fang, R. Li, and A. Sudjianto, Design and modeling for computer experiments, 2006.

A. Forrester, A. Sobester, and A. Keane, Engineering design via surrogate modelling: a practical guide, 2008.

G. Geffraye, O. Antoni, M. Farvacque, D. Kadri, G. Lavialle et al., CATHARE2 V2.5_2: A single version for various applications, Nuclear Engineering and Design, vol.241, pp.4456-4463, 2011.

A. Gretton, K. Fukumizu, C. Teo, L. Song, B. Schölkopf et al., A kernel statistical test of independence, Advances in neural information processing systems, pp.585-592, 2008.

G. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, Measuring statistical dependence with Hilbert-Schmidt norms, Proceedings Algorithmic Learning Theory, pp.63-77, 2005.

B. Iooss and A. Marrel, Advanced methodology for uncertainty propagation in computer experiments with large number of inputs, Nuclear Technology, vol.205, issue.12, pp.1588-1606, 2019.
URL : https://hal.archives-ouvertes.fr/cea-02339307

V. Larget, How to bring conservatism to a BEPU analysis, NURETH-18, 2019.

J. Lecoy, J. Sauvage, and C. Charignon, RIPS, a statistical method for characterizing the limiting scenario in a BEPU approach, Nuclear Technology, vol.205, issue.12, pp.1567-1577, 2019.

J. L. Loeppky, J. Sacks, and W. J. Welch, Choosing the sample size of a computer experiment: A practical guide, Technometrics, vol.51, pp.366-376, 2009.

A. Marrel and V. Chabridon, Statistical developments for target and conditional sensitivity analysis: application on safety studies for nuclear reactor, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02541142

A. Marrel, B. Iooss, S. Da, M. Veiga, and . Ribatet, Global sensitivity analysis of stochastic computer models with joint metamodels, Statistics and Computing, vol.22, pp.833-847, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00525489

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

C. E. Rasmussen and C. K. Williams, Gaussian processes for machine learning, 2006.

F. Sanchez-saez, A. I. Sànchez, J. F. Villanueva, S. Carlos, and S. Martorell, Uncertainty analysis of large break loss of coolant accident in a pressurized water reactor using nonparametric methods, Reliability Engineering and System Safety, vol.174, pp.19-28, 2018.

G. E. Wilson, Historical insights in the development of Best estimate Plus Uncertainty safety analysis, Annals of Nuclear Energy, vol.52, pp.2-9, 2013.