Structural reliability assessment through surrogate based importance sampling with dimension reduction
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[1] A. Kiureghian,et al. Optimization algorithms for structural reliability , 1991 .
[2] R. Dennis Cook. Save: a method for dimension reduction and graphics in regression , 2000 .
[3] H. Zha,et al. Contour regression: A general approach to dimension reduction , 2005, math/0508277.
[4] Ding Wang,et al. Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique , 2019, Reliab. Eng. Syst. Saf..
[5] B. Sudret,et al. Metamodel-based importance sampling for structural reliability analysis , 2011, 1105.0562.
[6] Costas Papadimitriou,et al. Reliability of uncertain dynamical systems with multiple design points , 1999 .
[7] Siu-Kui Au,et al. Augmenting approximate solutions for consistent reliability analysis , 2007 .
[8] Ling Li,et al. Sequential design of computer experiments for the estimation of a probability of failure , 2010, Statistics and Computing.
[9] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[10] R. Melchers. Importance sampling in structural systems , 1989 .
[11] J. Hammersley,et al. Monte Carlo Methods , 1965 .
[12] Anne Dutfoy,et al. Do Rosenblatt and Nataf isoprobabilistic transformations really differ , 2009 .
[13] Jing Li,et al. Evaluation of failure probability via surrogate models , 2010, J. Comput. Phys..
[14] S. Walker. Invited comment on the paper "Slice Sampling" by Radford Neal , 2003 .
[15] Nicolas Gayton,et al. AK-MCSi: A Kriging-based method to deal with small failure probabilities and time-consuming models , 2018, Structural Safety.
[16] Armen Der Kiureghian,et al. Design-point excitation for non-linear random vibrations , 2005 .
[17] R. Cook,et al. Sufficient Dimension Reduction and Graphics in Regression , 2002 .
[18] D. Ginsbourger,et al. Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling , 2011, 1111.6233.
[19] Victor Picheny,et al. Adaptive Designs of Experiments for Accurate Approximation of a Target Region , 2010 .
[20] Stefano Marelli,et al. Extending classical surrogate modelling to ultrahigh dimensional problems through supervised dimensionality reduction: a data-driven approach , 2018, ArXiv.
[21] Thomas J. Santner,et al. Design and analysis of computer experiments , 1998 .
[22] Alaa E. Mansour,et al. Extreme wave and wind response predictions , 2011 .
[23] Zhongming Jiang,et al. High dimensional structural reliability with dimension reduction , 2017 .
[24] Michael I. Jordan,et al. Kernel dimension reduction in regression , 2009, 0908.1854.
[25] Ker-Chau Li,et al. Sliced Inverse Regression for Dimension Reduction , 1991 .
[26] M. Shinozuka,et al. Simulation of Stochastic Processes by Spectral Representation , 1991 .
[27] Maurice Lemaire,et al. Assessing small failure probabilities by combined subset simulation and Support Vector Machines , 2011 .
[28] T. Choi,et al. Penalized Gaussian Process Regression and Classification for High‐Dimensional Nonlinear Data , 2011, Biometrics.
[29] Nicolas Gayton,et al. A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models , 2013, Reliab. Eng. Syst. Saf..
[30] Henrik O. Madsen,et al. Structural Reliability Methods , 1996 .
[31] Jian Wang,et al. LIF: A new Kriging based learning function and its application to structural reliability analysis , 2017, Reliab. Eng. Syst. Saf..
[32] A. Kiureghian,et al. Multiple design points in first and second-order reliability , 1998 .
[33] A. Kiureghian,et al. Multivariate distribution models with prescribed marginals and covariances , 1986 .
[34] J.-M. Bourinet,et al. Rare-event probability estimation with adaptive support vector regression surrogates , 2016, Reliab. Eng. Syst. Saf..
[35] V. Dubourg. Adaptive surrogate models for reliability analysis and reliability-based design optimization , 2011 .
[36] Nicolas Gayton,et al. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .
[37] Bruno Sudret,et al. The PHI2 method: a way to compute time-variant reliability , 2004, Reliab. Eng. Syst. Saf..
[38] Zhenzhou Lu,et al. A modified importance sampling method for structural reliability and its global reliability sensitivity analysis , 2018 .
[39] K. Fukumizu,et al. Gradient-Based Kernel Dimension Reduction for Regression , 2014 .
[40] N. Lelièvre. Développement des méthodes AK pour l'analyse de fiabilité. Focus sur les évènements rares et la grande dimension , 2018 .
[41] Sonja Kuhnt,et al. Design and analysis of computer experiments , 2010 .
[42] Anne Dutfoy,et al. A generalization of the Nataf transformation to distributions with elliptical copula , 2009 .
[43] Dirk P. Kroese,et al. Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.
[44] Ning Wang,et al. An improved reliability analysis approach based on combined FORM and Beta-spherical importance sampling in critical region , 2019 .
[45] J. Beck,et al. Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .
[46] Lambros S. Katafygiotis,et al. Geometric insight into the challenges of solving high-dimensional reliability problems , 2008 .