PC-Kriging: A new meta-modelling method and its application to quantile estimation
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[1] D. Ginsbourger,et al. Invariances of random fields paths, with applications in Gaussian Process Regression , 2013, 1308.1359.
[2] Bruno Sudret,et al. Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..
[3] Bruno Sudret,et al. Polynomial-Chaos-Kriging , 2014 .
[4] Olivier Dubrule,et al. Cross validation of kriging in a unique neighborhood , 1983 .
[5] R. Ghanem,et al. Stochastic Finite Elements: A Spectral Approach , 1990 .
[6] François Bachoc,et al. Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification , 2013, Comput. Stat. Data Anal..
[7] Carl E. Rasmussen,et al. Additive Gaussian Processes , 2011, NIPS.
[8] Bruno Sudret,et al. Meta-models for structural reliability and uncertainty quantification , 2012, 1203.2062.
[9] B. Sudret,et al. Metamodel-based importance sampling for structural reliability analysis , 2011, 1105.0562.
[10] Thomas J. Santner,et al. The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.
[11] M. Eldred. Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design , 2009 .
[12] B. Sudret,et al. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis , 2010 .
[13] Bruno Sudret,et al. Polynomial chaos expansions and stochastic finite element methods , 2015 .
[14] Bruno Sudret,et al. Adaptive sparse polynomial chaos expansion based on least angle regression , 2011, J. Comput. Phys..
[15] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[16] Nicolas Gayton,et al. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .
[17] M. D. Stefano,et al. Efficient algorithm for second-order reliability analysis , 1991 .