Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments, 2013. ,
URL : https://hal.archives-ouvertes.fr/tel-00881002
The Numerical Treatment of Integral Equations, Journal of Applied Mechanics, vol.46, issue.4, 1977. ,
DOI : 10.1115/1.3424708
Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.34, issue.4, pp.773-793, 2012. ,
DOI : 10.2307/1269548
URL : https://hal.archives-ouvertes.fr/hal-00689580
Objective Bayesian Analysis of Spatially Correlated Data, Journal of the American Statistical Association, vol.96, issue.456, pp.1361-1374, 2001. ,
DOI : 10.1198/016214501753382282
Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions, AIAA Journal, vol.26, issue.2, pp.2459-2468, 2008. ,
DOI : 10.1109/JMEMS.2004.825308
Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set, Technometrics, vol.13, issue.4, pp.455-465, 2014. ,
DOI : 10.1007/3-540-50871-6
URL : https://hal.archives-ouvertes.fr/hal-00641108
AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation, Structural Safety, vol.33, issue.2, pp.145-154, 2011. ,
DOI : 10.1016/j.strusafe.2011.01.002
Design and modeling for computer experiments, Computer Science and Data Analysis Series, vol.8, 2006. ,
DOI : 10.1201/9781420034899
Ch. 4. Uniform experimental designs and their applications in industry, Handbook of Statistics, vol.22, pp.131-178, 2003. ,
DOI : 10.1016/S0169-7161(03)22006-X
URL : https://hal.archives-ouvertes.fr/hal-01541536
Computational Intelligence in Expensive Optimization Problems, volume 2 of Adaptation Learning and Optimization , chapter Kriging Is Well-Suited to Parallelize Optimization, pp.131-162, 2010. ,
Gaussian Process Single-Index Models as Emulators for Computer Experiments, Technometrics, vol.35, issue.6, pp.30-41, 2012. ,
DOI : 10.1016/j.csda.2008.12.010
URL : http://arxiv.org/pdf/1009.4241
Cases for the nugget in modeling computer experiments, Statistics and Computing, vol.4, issue.4, pp.713-722, 2012. ,
DOI : 10.1007/978-1-4612-1494-6
Sequential Design for Ranking Response Surfaces, SIAM/ASA Journal on Uncertainty Quantification, vol.5, issue.1, pp.212-239, 2017. ,
DOI : 10.1137/15M1045168
Predicting the output from a complex computer code when fast approximations are available, Biometrika, vol.87, issue.1, pp.1-13, 2000. ,
DOI : 10.1093/biomet/87.1.1
Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.3, pp.425-464, 2001. ,
DOI : 10.1111/1467-9868.00294
Regression and kriging metamodels with their experimental designs in simulation: A review, European Journal of Operational Research, vol.256, pp.1-16, 2017. ,
Interpolation of Spatial Data: Some Theory for Kriging, 1999. ,
DOI : 10.1007/978-1-4612-1494-6
Default priors for Gaussian processes, The Annals of Statistics, vol.33, issue.2, pp.556-582, 2005. ,
DOI : 10.1214/009053604000001264
URL : http://doi.org/10.1214/009053604000001264
Active learning surrogate models for the conception of systems with multiple failure modes, Reliability Engineering & System Safety, vol.149, pp.130-136, 2016. ,
DOI : 10.1016/j.ress.2015.12.017
URL : https://hal.archives-ouvertes.fr/hal-01266534
A repulsion-based method for the definition and the enrichment of opotimized space filling designs in constrained input spaces, Journal de la Société Française de Statistique, vol.158, issue.1, pp.37-67, 2017. ,
Nested polynomial trends for the improvement of Gaussian process-based predictors, Journal of Computational Physics, vol.346, pp.389-402, 2017. ,
DOI : 10.1016/j.jcp.2017.05.051
URL : https://hal.archives-ouvertes.fr/hal-01298861
Gaussian Processes in Machine Learning, 2006. ,
DOI : 10.1162/089976602317250933
URL : http://mlg.eng.cam.ac.uk/pub/pdf/Ras04.pdf
The Bayesian Choice, 2007. ,
DOI : 10.1007/978-1-4757-4314-2
Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-435, 1989. ,
DOI : 10.1214/ss/1177012413
The design and analysis of computer experiments. Springer series in statistics, 2003. ,