A global sensitivity analysis framework for hybrid simulation, Mechanical Systems and Signal Processing, vol.146, p.106997, 2021. ,
URL : https://hal.archives-ouvertes.fr/hal-02337030
Stochastic finite elements: a non intrusive approach by regression, European Journal of Mechanics, vol.15, issue.1-3, pp.81-92, 2006. ,
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. ,
Adaptive sparse polynomial chaos expansion based on Least Angle Regression, Journal of Computational Physics, vol.230, pp.2345-2367, 2011. ,
Rare-event probability estimation with adaptive support vector regression surrogates, Reliability Engineering & System Safety, vol.150, pp.210-221, 2016. ,
Classification and regression trees, 2017. ,
, Sparse grids, vol.13, pp.147-269, 2004.
An introduction to compressive sampling: A sensing/sampling paradigm that goes against the common knowledge in data acquisition, IEEE Signal Processing Magazine, vol.25, issue.2, pp.21-30, 2008. ,
Model selection for small sample regression, Journal of Machine Learning Research, vol.48, issue.1, pp.9-23, 2002. ,
Bart: Bayesian additive regression trees, Annals of Applied Statistics, vol.4, issue.1, pp.266-298, 2010. ,
Compressed sensing, IEEE Transactions on information theory, vol.52, issue.4, pp.1289-1306, 2006. ,
URL : https://hal.archives-ouvertes.fr/inria-00369486
Adaptive surrogate models for reliability analysis and reliability-based design optimization, 2011. ,
URL : https://hal.archives-ouvertes.fr/tel-00697026
On the convergence of generalized polynomial chaos expansions, ESAIM: Mathematical Modelling and Numerical Analysis, vol.46, issue.02, pp.317-339, 2012. ,
The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications, Journal of Computational Physics, vol.227, issue.22, pp.9572-9595, 2008. ,
Multivariate adaptive regression splines, Annals of Statistics, vol.19, issue.1, pp.1-67, 1991. ,
Orthogonal polynomials: computation and approximation, 2004. ,
Deep Learning, 2016. ,
Kriging based reliability and sensitivity analysis-application to the stability of an earth dam, Computers and Geotechnics, vol.120, p.103411, 2020. ,
Uncertainty quantification and global sensitivity analysis for economic models, Quantitative Economics, vol.10, pp.1-41, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01902025
Snap-through instability patterns in truss structures, p.51, 2010. ,
, Structural Dynamics, and Materials Conference 18th AIAA/ASME/AHS Adaptive Structures Conference 12th, p.2611
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. ,
URL : https://hal.archives-ouvertes.fr/hal-01143146
Extending classical surrogate modeling to high dimensions through supervised dimensionality reduction: a data-driven approach, International Journal for Uncertainty Quantification, vol.10, issue.1, pp.55-82, 2020. ,
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes, Handbook on Uncertainty Quantification, vol.38, pp.1289-1325, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01428947
A multivariate exponentially weighted moving average control chart, Technometrics, vol.34, issue.1, pp.46-53, 1992. ,
, Sparse polynomial chaos expansions: Literature survey and benchmark, 2020.
Multi-resolution analysis of Wienertype uncertainty propagation schemes, Journal of Computational Physics, vol.197, issue.2, pp.502-531, 2004. ,
UQLab: A framework for uncertainty quantification in Matlab, Vulnerability, Uncertainty, and Risk (Proc. 2nd Int. Conf. on Vulnerability, Risk Analysis and Management (ICVRAM2014), pp.2554-2563, 2014. ,
An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis, Structural Safety, vol.75, pp.67-74, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01902018
UQLab user manual -Polynomial chaos expansions, Chair of Risk, Safety and Uncertainty Quantification, 2019. ,
A two-stage surrogate modelling approach for the approximation of models with non-smooth outputs, 3rd ECCOMAS thematic conference on Uncertainty Quantification in Computational Sciences and Engineering, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02379136
Comparative study of Kriging and support vector regression for structural engineering applications, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol.4, issue.2, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01893274
Spectral likelihood expansions for Bayesian inference, Journal of Computational Physics, vol.309, pp.267-294, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01432170
Generalized linear models, Journal of the Royal Statistical Society. Series A (General), vol.135, issue.3, pp.370-384, 1972. ,
fPINNs: Fractional physics-informed neural networks, SIAM Journal on Scientific Computing, vol.41, issue.4, pp.2603-2626, 2019. ,
Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling, Nuclear Engineering and Technology, vol.52, issue.2, pp.287-295, 2020. ,
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics, vol.378, pp.686-707, 2019. ,
Adaptive computation and machine learning, 2006. ,
Smoothing by spline functions, Numerische mathematik, vol.10, issue.3, pp.177-183, 1967. ,
Remarks on a multivariate transformation, Annals of Mathematical Statistics, vol.23, issue.3, pp.470-472, 1952. ,
Poincaré inequalities on intervals -application to sensitivity analysis, Electronic journal of statistics, vol.11, issue.2, pp.3081-3119, 2017. ,
The Design and Analysis of Computer Experiments, 2003. ,
Polynomial-chaos-based Kriging, International Journal for Uncertainty Quantification, vol.5, issue.2, pp.171-193, 2015. ,
Advanced surrogate models for multidisciplinary design optimization, 2009. ,
Adaptive monte carlo analysis for strongly nonlinear stochastic systems, Reliability Engineering & System Safety, vol.175, pp.207-224, 2018. ,
Surrogate model uncertainty in wind turbine reliability assessment, Renewable Energy, vol.151, pp.1150-1162, 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02294637
Sensitivity estimates for nonlinear mathematical models, Mathematical and Computer Modelling, vol.1, pp.407-414, 1993. ,
Global sensitivity analysis using polynomial chaos expansions, Reliability Engineering and System Safety, vol.93, pp.964-979, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-01432217
Data-driven polynomial chaos expansion for machine learning regression, Journal of Computational Physics, vol.388, pp.601-623, 2019. ,
A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas, Probabilistic Engineering Mechanics, vol.55, pp.1-16, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01901982
The nature of statistical learning theory, 2013. ,
Bayesian model calibration with stochastic spectral embedding, Journal of Computational Physics, 2020. ,
Multi-element generalized polynomial chaos for arbitrary probability measures, SIAM Journal on Scientific Computing, vol.28, issue.3, pp.901-928, 2006. ,
Quantitative evaluations of uncertainties in multivariate operations of microgrids, IEEE Transactions on Smart Grid, 2020. ,
The Wiener-Askey polynomial chaos for stochastic differential equations, SIAM Journal on Scientific Computing, vol.24, issue.2, pp.619-644, 2002. ,
Adaptive importance sampling for integration, 1998. ,