Quasi-regression, Journal of Complexity, vol.17, issue.4, pp.588-607, 2001. ,
DOI : 10.1006/jcom.2001.0588
URL : http://doi.org/10.1006/jcom.2001.0588
Evaluation d'un risque d'inondation fluviale par planification séquentielle d'expériences, 42èmes Journées de Statistique, pp.1-6, 2010. ,
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 : 10.1007/s11222-011-9241-4
URL : https://hal.archives-ouvertes.fr/hal-00689580
Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions, AIAA Journal, vol.46, issue.10, pp.2459-2468, 2008. ,
DOI : 10.2514/1.34321
Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach, Comptes Rendus M??canique, vol.336, issue.6, pp.518-523, 2008. ,
DOI : 10.1016/j.crme.2008.02.013
An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis, Probabilistic Engineering Mechanics, vol.25, issue.2, pp.183-197, 2010. ,
DOI : 10.1016/j.probengmech.2009.10.003
Adaptive sparse polynomial chaos expansion based on least angle regression, Journal of Computational Physics, vol.230, issue.6, pp.2345-2367, 2011. ,
DOI : 10.1016/j.jcp.2010.12.021
Assessing small failure probabilities by combined subset simulation and Support Vector Machines, Structural Safety, vol.33, issue.6, pp.343-353, 2011. ,
DOI : 10.1016/j.strusafe.2011.06.001
An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability, Reliability Engineering & System Safety, vol.131, pp.109-117, 2014. ,
DOI : 10.1016/j.ress.2014.06.023
Wavelet density-based adaptive importance sampling method, Structural Safety, vol.52, pp.161-169, 2014. ,
DOI : 10.1016/j.strusafe.2014.02.003
Stochastic linear optimization under bandit feedback, The 21st Annual Conference on Learning Theory, 2008. ,
Uncertainty in industrial practice ? A guide to quantitative uncertainty management, 2008. ,
Adaptive surrogate models for reliability analysis and reliability-based design optimization, 2011. ,
URL : https://hal.archives-ouvertes.fr/tel-00697026
Meta-model-based importance sampling for reliability sensitivity analysis, Structural Safety, vol.49, pp.27-36, 2014. ,
DOI : 10.1016/j.strusafe.2013.08.010
Metamodel-based importance sampling for structural reliability analysis, Probabilistic Engineering Mechanics, vol.33, pp.47-57, 2013. ,
DOI : 10.1016/j.probengmech.2013.02.002
URL : https://hal.archives-ouvertes.fr/hal-00590604
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
A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models, Reliability Engineering & System Safety, vol.111, pp.232-240, 2013. ,
DOI : 10.1016/j.ress.2012.10.008
Least angle regression, Annals of Statistics, vol.32, pp.407-499, 2004. ,
Stochastic Finite Elements: A Spectral Approach, 2003. ,
DOI : 10.1007/978-1-4612-3094-6
Distance-based kriging relying on proxy simulations for inverse conditioning, Advances in Water Resources, vol.52, pp.275-291, 2013. ,
DOI : 10.1016/j.advwatres.2012.11.019
URL : https://hal.archives-ouvertes.fr/hal-00698582
Support vector machines for classification and regression, 1998. ,
Sensitivity/uncertainty analysis of a borehole scenario comparing Latin hypercube sampling and deterministic sensitivity approaches, 1983. ,
Neural-network-based reliability analysis: a comparative study, Computer Methods in Applied Mechanics and Engineering, vol.191, issue.1-2, pp.113-132, 2001. ,
DOI : 10.1016/S0045-7825(01)00248-1
Plans d'expériences adaptatifs pour le calcul de quantiles et applicationàapplicationà la dosimétrie numérique, 2013. ,
Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998. ,
DOI : 10.1023/A:1008306431147
Application of kriging method to structural reliability problems, Structural Safety, vol.27, issue.2, pp.133-151, 2005. ,
DOI : 10.1016/j.strusafe.2004.09.001
A new surrogate modeling technique combining Kriging and polynomial chaos expansions ??? Application to uncertainty analysis in computational dosimetry, Journal of Computational Physics, vol.286, pp.103-117, 2015. ,
DOI : 10.1016/j.jcp.2015.01.034
URL : https://hal.archives-ouvertes.fr/hal-01143146
A generalization of the Nataf transformation to distributions with elliptical copula, Probabilistic Engineering Mechanics, vol.24, issue.2, pp.172-178, 2009. ,
DOI : 10.1016/j.probengmech.2008.05.001
An innovating analysis of the Nataf transformation from the copula viewpoint, Probabilistic Engineering Mechanics, vol.24, issue.3, pp.312-320, 2009. ,
DOI : 10.1016/j.probengmech.2008.08.001
Response surface augmented moment method for efficient reliability analysis, Structural Safety, vol.28, issue.3, pp.261-272, 2006. ,
DOI : 10.1016/j.strusafe.2005.08.003
Structural reliability, 2009. ,
DOI : 10.1002/9780470611708
UQLab: A Framework for Uncertainty Quantification in Matlab, Vulnerability, Uncertainty, and Risk, pp.2554-2563, 2014. ,
DOI : 10.1061/9780784413609.257
Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction, Technometrics, vol.15, issue.3, pp.243-255, 1993. ,
DOI : 10.1080/00401706.1992.10485229
Sequential Experiment Design for Contour Estimation From Complex Computer Codes, Technometrics, vol.50, issue.4, pp.527-541, 2012. ,
DOI : 10.1198/004017008000000541
Gaussian processes for machine learning Adaptive computation and machine learning, 2006. ,
The Design and Analysis of Computer Experiments, 2003. ,
DOI : 10.1007/978-1-4757-3799-8
PC-Kriging: a new metamodelling method combining polynomial chaos expansions and Kriging, Proc. 2nd Int. Symposium on Uncertainty Quantification and Stochastic Modeling, 2014. ,
POLYNOMIAL-CHAOS-BASED KRIGING, International Journal for Uncertainty Quantification, vol.5, issue.2, pp.171-193, 2015. ,
DOI : 10.1615/Int.J.UncertaintyQuantification.2015012467
Benefit of splines and neural networks in simulation based structural reliability analysis, Structural Safety, vol.27, issue.3, pp.246-261, 2005. ,
DOI : 10.1016/j.strusafe.2004.11.001
Use of Kriging as meta-model in simulation procedures for structural reliability, Proc. 9th Int. Conf. Struct. Safety and Reliability (ICOSSAR'2005), pp.2483-2490, 2005. ,
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting, IEEE Transactions on Information Theory, vol.58, issue.5, pp.3250-3265, 2012. ,
DOI : 10.1109/TIT.2011.2182033
Uncertainty propagation and sensitivity analysis in mechanical models ? contributions to structural reliability and stochastic spectral methods, 2007. ,
Quasi random numbers in stochastic finite element analysis ? Application to global sensitivity analysis, Proc. 10th Int. Conf. on Applications of Stat. and Prob. in Civil Engineering (ICASP10), 2007. ,
Structural reliability using finite element methods: an appraisal of DARS: Directional Adaptive Response Surface Sampling, 2000. ,
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations, SIAM Journal on Scientific Computing, vol.24, issue.2, pp.619-644, 2002. ,
DOI : 10.1137/S1064827501387826
URL : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA460654
Data Mining and Analysis: fundamental Concepts and Algorithms, 2014. ,
Two Improved Algorithms for Reliability Analysis, 1995. ,
DOI : 10.1007/978-0-387-34866-7_32