J. Lee, Y. Yang, and W. Ruy, A comparative study on reliabilityindex and target-performance-based probabilistic structural design optimization, Computers & Structures, vol.80, issue.3, pp.257-269, 2002.

Q. Zhao, X. Chen, Z. Ma, and Y. Lin, A comparison of deterministic, reliability-based topology optimization under uncertainties, Acta Mechanica Solida Sinica, vol.29, issue.1, pp.31-45, 2016.

H. A. Jensen, M. A. Valdebenito, G. I. Schuëller, and D. S. Kusanovic, Reliability-based optimization of stochastic systems using line search, Computer Methods in Applied Mechanics and Engineering, vol.198, issue.49, pp.3915-3924, 2009.

M. A. Valdebenito and G. I. Schuëller, Efficient strategies for reliability-based optimization involving non-linear, dynamical structures, Computers & Structures, vol.89, issue.19, pp.1797-1811, 2011.

M. Papadrakakis and N. D. Lagaros, Reliability-based structural optimization using neural networks and monte carlo simulation, Computer Methods in Applied Mechanics and Engineering, vol.191, issue.32, pp.3491-3507, 2002.

V. Keshavarzzadeh, F. Fernandez, and D. A. Tortorelli, Topology optimization under uncertainty via non-intrusive polynomial chaos expansion, Computer Methods in Applied Mechanics and Engineering, vol.318, pp.120-147, 2017.

R. Schöbi, B. Sudret, and S. Marelli, Rare event estimation using polynomial-chaos kriging, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol.3, issue.2, p.4016002, 2017.

J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.22, issue.3, pp.773-793, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00689580

J. Ching and W. Hsu, Approximate optimization of systems with highdimensional uncertainties and multiple reliability constraints, Computational Methods in Optimization Considering Uncertainties, vol.198, pp.52-71, 2008.

J. Xu, W. Zhang, and R. Sun, Efficient reliability assessment of structural dynamic systems with unequal weighted quasi-monte carlo simulation, Computers & Structures, vol.175, pp.37-51, 2016.

O. Amir, O. Sigmund, S. Boyan, M. Lazarov, and . Schevenels, Efficient reanalysis techniques for robust topology optimization, Computer Methods in Applied Mechanics and Engineering, pp.217-231, 2012.

N. Chen, D. Yu, B. Xia, and Z. Ma, Topology optimization of structures with interval random parameters, Computer Methods in Applied Mechanics and Engineering, vol.307, pp.300-315, 2016.

I. Doltsinis, Z. Kang, and G. Cheng, Robust design of non-linear structures using optimization methods, Special Issue on Computational Methods in Stochastic Mechanics and Reliability Analysis, vol.194, issue.12, pp.1779-1795, 2005.

Z. Tang and J. Périaux, Uncertainty based robust optimization method for drag minimization problems in aerodynamics, Computer Methods in Applied Mechanics and Engineering, pp.12-24, 2012.

A. A. Taflanidis and J. L. Beck, An efficient framework for optimal robust stochastic system design using stochastic simulation, Computer Methods in Applied Mechanics and Engineering, vol.198, issue.1, pp.88-101, 2008.

C. Juan, A. A. Medina, and . Taflanidis, Adaptive importance sampling for optimization under uncertainty problems, Computer Methods in Applied Mechanics and Engineering, vol.279, pp.133-162, 2014.

I. Elishakoff, R. T. Haftka, and J. Fang, Structural design under bounded uncertainty-optimization with anti-optimization, Computers & Structures, vol.53, issue.6, pp.1401-1405, 1994.

S. Mcwilliam, Anti-optimisation of uncertain structures using interval analysis. Computers & Structures, vol.79, pp.421-430, 2001.

J. Zhang, A. A. Taflanidis, and J. C. Medina, Sequential approximate optimization for design under uncertainty problems utilizing kriging metamodeling in augmented input space, Computer Methods in Applied Mechanics and Engineering, vol.315, pp.369-395, 2017.

R. Jin, X. Du, and W. Chen, The use of metamodeling techniques for optimization under uncertainty. Structural and Multidisciplinary Optimization, vol.25, pp.99-116, 2003.

K. Lee and G. Park, A global robust optimization using kriging based approximation model, JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, vol.49, issue.3, pp.779-788, 2006.

G. Dellino, P. C. Jack, C. Kleijnen, and . Meloni, Robust optimization in simulation: Taguchi and krige combined, INFORMS Journal on Computing, vol.24, issue.3, pp.471-484, 2012.

M. Eldred, A. Giunta, S. Wojtkiewicz, and T. Trucano, Formulations for Surrogate-Based Optimization Under Uncertainty, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Multidisciplinary Analysis Optimization Conferences, 2002.

J. Janusevskis and R. Le-riche, Simultaneous kriging-based estimation and optimization of mean response, Journal of Global Optimization, vol.55, issue.2, pp.313-336, 2013.
URL : https://hal.archives-ouvertes.fr/emse-00674460

R. Le-riche, V. Picheny, A. Meyer, N. Kim, and D. Ginsbourger, Gears design with shape uncertainties using controlled monte carlo simulations and kriging, 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 17th AIAA/ASME/AHS Adaptive Structures Conference 11th AIAA, p.2257, 2009.

M. Binois, D. Ginsbourger, and O. Roustant, Quantifying uncertainty on pareto fronts with gaussian process conditional simulations, European journal of operational research, vol.243, issue.2, pp.386-394, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00904811

D. Huang, W. T-t-allen, N. Notz, and . Zeng, Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models, Journal of Global Optimization, vol.34, issue.3, pp.441-466, 2006.

V. Picheny, D. Ginsbourger, and Y. Richet, Noisy Expected Improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00489321

J. Teich, Pareto-Front Exploration with Uncertain Objectives, pp.314-328, 2001.

M. Mlakar, T. Tusar, and B. Filipic, Comparing solutions under uncertainty in multiobjective optimization, Mathematical Problems in Engineering, pp.1-10, 2014.

F. Fusi and P. M. Congedo, An adaptive strategy on the error of the objective functions for uncertainty-based derivative-free optimization, Journal of Computational Physics, vol.309, pp.241-266, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01378411

M. Rivier and P. M. Congedo, Surrogate-Assisted Bounding-Box Approach for Optimization Problems with Approximated Objectives, Inria, 2018.

. Sébastien-le-digabel, Nomad: Nonlinear optimization with the mads algorithm, vol.37, p.44, 2011.

. Gpy, GPy: A gaussian process framework in python

C. E. Rasmussen, Gaussian processes in machine learning, Advanced lectures on machine learning, pp.63-71, 2004.

M. P. Dubuisson and A. K. Jain, A modified hausdorff distance for object matching, Proceedings of 12th International Conference on Pattern Recognition, vol.1, pp.566-568, 1994.

V. Baudoui, ;. Jean-baptiste, and . Harran-klotz, Optimisation robuste multiobjectifs par modèles de substitution, Patricia Mathématiques appliquées et énergétique et transferts Toulouse, 2012.

J. Lachaud and N. N. Mansour, Porous material analysis toolbox based on openfoam and applications, Journal of Thermophysics and Heat Transfer, vol.28, issue.2, pp.191-202, 2014.

M. Rivier, J. Lachaud, and P. M. Congedo, Ablative thermal protection system under uncertainties including pyrolysis gas composition, Aerospace Science and Technology, vol.84, pp.1059-1069, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01791072