暂无分享,去创建一个
[1] Roger G. Ghanem,et al. Basis adaptation in homogeneous chaos spaces , 2014, J. Comput. Phys..
[2] Stefano Marelli,et al. EXTENDING CLASSICAL SURROGATE MODELING TO HIGH DIMENSIONS THROUGH SUPERVISED DIMENSIONALITY REDUCTION: A DATA-DRIVEN APPROACH , 2018 .
[3] Naima Kaabouch,et al. Compressive sensing: Performance comparison of sparse recovery algorithms , 2018, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).
[4] R. Ghanem,et al. Stochastic Finite Elements: A Spectral Approach , 1990 .
[5] Y. C. Pati,et al. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.
[6] Amir H. Gandomi,et al. Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels , 2020 .
[7] J. Kiefer,et al. Optimum Designs in Regression Problems , 1959 .
[8] Emmanuel J. Candès,et al. A Probabilistic and RIPless Theory of Compressed Sensing , 2010, IEEE Transactions on Information Theory.
[9] H. Rabitz,et al. D-MORPH regression: application to modeling with unknown parameters more than observation data , 2010 .
[10] S. Isukapalli. UNCERTAINTY ANALYSIS OF TRANSPORT-TRANSFORMATION MODELS , 1999 .
[11] Luc Pronzato,et al. Design of computer experiments: space filling and beyond , 2011, Statistics and Computing.
[12] Olgica Milenkovic,et al. Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.
[13] Margaret J. Robertson,et al. Design and Analysis of Experiments , 2006, Handbook of statistics.
[14] A. Kiureghian,et al. OPTIMAL DISCRETIZATION OF RANDOM FIELDS , 1993 .
[15] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[16] S. Frick,et al. Compressed Sensing , 2014, Computer Vision, A Reference Guide.
[17] Bhaskar D. Rao,et al. Sparse Bayesian learning for basis selection , 2004, IEEE Transactions on Signal Processing.
[18] R. D. Cook,et al. A Comparison of Algorithms for Constructing Exact D-Optimal Designs , 1980 .
[19] Byeng D. Youn,et al. Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems , 2011 .
[20] Zhenzhou Lu,et al. Sparse polynomial chaos expansions for global sensitivity analysis with partial least squares and distance correlation , 2018, Structural and Multidisciplinary Optimization.
[21] Xiu Yang,et al. Reweighted ℓ1ℓ1 minimization method for stochastic elliptic differential equations , 2013, J. Comput. Phys..
[22] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[23] Alexander Tarakanov,et al. Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models , 2019, J. Comput. Phys..
[24] B. Sudret,et al. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis , 2010 .
[25] Gary Tang,et al. Subsampled Gauss Quadrature Nodes for Estimating Polynomial Chaos Expansions , 2014, SIAM/ASA J. Uncertain. Quantification.
[26] José Mario Martínez,et al. Nonmonotone Spectral Projected Gradient Methods on Convex Sets , 1999, SIAM J. Optim..
[27] I. Sobol. On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .
[28] Stefano Marelli,et al. Sequential Design of Experiment for Sparse Polynomial Chaos Expansions , 2017, SIAM/ASA J. Uncertain. Quantification.
[29] D. Xiu,et al. STOCHASTIC COLLOCATION ALGORITHMS USING 𝓁 1 -MINIMIZATION , 2012 .
[30] Peter E. Thornton,et al. DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING , 2014 .
[31] Dongbin Xiu,et al. On a near optimal sampling strategy for least squares polynomial regression , 2016, J. Comput. Phys..
[32] Yoshua Bengio,et al. Model Selection for Small Sample Regression , 2002, Machine Learning.
[33] Paul Diaz,et al. Sparse polynomial chaos expansions via compressed sensing and D-optimal design , 2017, Computer Methods in Applied Mechanics and Engineering.
[34] H. Meidani,et al. A preconditioning approach for improved estimation of sparse polynomial chaos expansions , 2017, Computer Methods in Applied Mechanics and Engineering.
[35] David Zhang,et al. A Survey of Sparse Representation: Algorithms and Applications , 2015, IEEE Access.
[36] Yan Shi,et al. An expanded sparse Bayesian learning method for polynomial chaos expansion , 2019, Mechanical Systems and Signal Processing.
[37] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.
[38] Joe Wiart,et al. Surrogate modeling based on resampled polynomial chaos expansions , 2018, Reliab. Eng. Syst. Saf..
[39] Xun Huan,et al. Compressive sensing adaptation for polynomial chaos expansions , 2018, J. Comput. Phys..
[40] Bruno Sudret,et al. Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..
[41] Alan J. Miller,et al. A review of some exchange algorithms for constructing discrete D-optimal designs , 1992 .
[42] Sankaran Mahadevan,et al. Effectively Subsampled Quadratures for Least Squares Polynomial Approximations , 2016, SIAM/ASA J. Uncertain. Quantification.
[43] Zhenzhou Lu,et al. An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework , 2019, Computer Methods in Applied Mechanics and Engineering.
[44] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[45] Holger Rauhut,et al. Sparse Legendre expansions via l1-minimization , 2012, J. Approx. Theory.
[46] Deqiang Gan,et al. Polynomial Chaos Expansion for Parametric Problems in Engineering Systems: A Review , 2020, IEEE Systems Journal.
[47] Ivan V. Oseledets,et al. Rectangular maximum-volume submatrices and their applications , 2015, ArXiv.
[48] Bruno Sudret,et al. Global sensitivity analysis using low-rank tensor approximations , 2016, Reliab. Eng. Syst. Saf..
[49] Mário A. T. Figueiredo. Adaptive Sparseness for Supervised Learning , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[50] Michael Ludkovski,et al. Sequential Design for Ranking Response Surfaces , 2015, SIAM/ASA J. Uncertain. Quantification.
[51] Bhaskar D. Rao,et al. Perspectives on Sparse Bayesian Learning , 2003, NIPS.
[52] Dongbin Xiu,et al. High-Order Collocation Methods for Differential Equations with Random Inputs , 2005, SIAM J. Sci. Comput..
[53] Akil C. Narayan,et al. A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions , 2016, SIAM J. Sci. Comput..
[54] Richard J. Beckman,et al. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.
[55] O. Dykstra. The Augmentation of Experimental Data to Maximize [X′X] , 1971 .
[56] Xiu Yang,et al. Sliced-Inverse-Regression-Aided Rotated Compressive Sensing Method for Uncertainty Quantification , 2017, SIAM/ASA J. Uncertain. Quantification.
[57] Hadi Meidani,et al. A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions , 2017, J. Comput. Phys..
[58] Fang Xu,et al. An efficient adaptive forward–backward selection method for sparse polynomial chaos expansion , 2019, Computer Methods in Applied Mechanics and Engineering.
[59] Ivan V. Oseledets,et al. Gradient Descent-based D-optimal Design for the Least-Squares Polynomial Approximation , 2018, ArXiv.
[60] Bruno Sudret,et al. Adaptive sparse polynomial chaos expansion based on least angle regression , 2011, J. Comput. Phys..
[61] Xun Huan,et al. Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions , 2017, SIAM/ASA J. Uncertain. Quantification.
[62] Alireza Doostan,et al. Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies , 2014, J. Comput. Phys..
[63] Bruno Sudret,et al. Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach , 2008 .
[64] Khachik Sargsyan,et al. Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection , 2014, J. Comput. Phys..
[65] Kyle A. Gallivan,et al. A compressed sensing approach for partial differential equations with random input data , 2012 .
[66] Alireza Doostan,et al. Basis adaptive sample efficient polynomial chaos (BASE-PC) , 2017, J. Comput. Phys..
[67] Samih Zein,et al. An Efficient Sampling Method for Regression-Based Polynomial Chaos Expansion , 2013 .
[68] Lawrence Carin,et al. Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.
[69] Joe Wiart,et al. SURROGATE MODELING OF INDOOR DOWN-LINK HUMAN EXPOSURE BASED ON SPARSE POLYNOMIAL CHAOS EXPANSION , 2019, International Journal for Uncertainty Quantification.
[70] Matthias W. Seeger,et al. Compressed sensing and Bayesian experimental design , 2008, ICML '08.
[71] David Cohn,et al. Active Learning , 2010, Encyclopedia of Machine Learning.
[72] A. Owen. Controlling correlations in latin hypercube samples , 1994 .
[73] Stephen P. Boyd,et al. Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.
[74] M. Lemaire,et al. Stochastic finite element: a non intrusive approach by regression , 2006 .
[75] Aggelos K. Katsaggelos,et al. Bayesian Compressive Sensing Using Laplace Priors , 2010, IEEE Transactions on Image Processing.
[76] Prasanth B. Nair,et al. Some greedy algorithms for sparse polynomial chaos expansions , 2019, J. Comput. Phys..
[77] O. Ernst,et al. ON THE CONVERGENCE OF GENERALIZED POLYNOMIAL CHAOS EXPANSIONS , 2011 .
[78] Thierry A. Mara,et al. Bayesian sparse polynomial chaos expansion for global sensitivity analysis , 2017 .
[79] Dongbin Xiu,et al. Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression , 2016, SIAM J. Sci. Comput..
[80] Hadi Meidani,et al. Divide and conquer: An incremental sparsity promoting compressive sampling approach for polynomial chaos expansions , 2016, 1606.06611.
[81] Tao Zhou,et al. A Christoffel function weighted least squares algorithm for collocation approximations , 2014, Math. Comput..
[82] Ashutosh Kumar Singh,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .
[83] C. Pan,et al. Rank-Revealing QR Factorizations and the Singular Value Decomposition , 1992 .
[84] Michael P. Friedlander,et al. Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..
[85] Houman Owhadi,et al. A non-adapted sparse approximation of PDEs with stochastic inputs , 2010, J. Comput. Phys..
[86] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[87] Jack Xin,et al. Minimization of ℓ1-2 for Compressed Sensing , 2015, SIAM J. Sci. Comput..
[88] Ming Gu,et al. Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization , 1996, SIAM J. Sci. Comput..
[89] Iason Papaioannou,et al. PLS-based adaptation for efficient PCE representation in high dimensions , 2019, J. Comput. Phys..
[90] Michael D. Shields,et al. The generalization of Latin hypercube sampling , 2015, Reliab. Eng. Syst. Saf..
[91] Stefano Marelli,et al. UQLab: a framework for uncertainty quantification in MATLAB , 2014 .
[92] W. V. Harper,et al. Sensitivity/uncertainty analysis of a borehole scenario comparing Latin Hypercube Sampling and deterministic sensitivity approaches , 1983 .
[93] Michael Elad,et al. From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..
[94] A. Doostan,et al. Least squares polynomial chaos expansion: A review of sampling strategies , 2017, 1706.07564.
[95] Francesco Contino,et al. A robust and efficient stepwise regression method for building sparse polynomial chaos expansions , 2017, J. Comput. Phys..
[96] Michael B. Wakin,et al. An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition] , 2008 .
[97] Zhenzhou Lu,et al. Sparse polynomial chaos expansion based on D-MORPH regression , 2018, Appl. Math. Comput..
[98] Robert D. Nowak,et al. Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior , 2001, IEEE Trans. Image Process..
[99] Alireza Doostan,et al. A weighted l1-minimization approach for sparse polynomial chaos expansions , 2013, J. Comput. Phys..
[100] Michael E. Tipping,et al. Analysis of Sparse Bayesian Learning , 2001, NIPS.
[101] Tao Zhou,et al. Multivariate Discrete Least-Squares Approximations with a New Type of Collocation Grid , 2014, SIAM J. Sci. Comput..
[102] Michael E. Tipping,et al. Fast Marginal Likelihood Maximisation for Sparse Bayesian Models , 2003 .
[103] Sungyoung Lee,et al. Compressive sensing: From theory to applications, a survey , 2013, Journal of Communications and Networks.
[104] Dongbin Xiu,et al. The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations , 2002, SIAM J. Sci. Comput..
[105] Yuhang Chen,et al. Stochastic collocation methods via $L_1$ minimization using randomized quadratures , 2016, 1602.00995.
[106] J. Halton. On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .
[107] Zhenzhou Lu,et al. Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression , 2018 .
[108] Knut Baumann,et al. Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation , 2014, Journal of Cheminformatics.
[109] Wei Zhao,et al. Global sensitivity analysis with a hierarchical sparse metamodeling method , 2019, Mechanical Systems and Signal Processing.
[110] Danny Lathouwers,et al. Grid and basis adaptive polynomial chaos techniques for sensitivity and uncertainty analysis , 2014, J. Comput. Phys..
[111] Alireza Doostan,et al. Coherence motivated sampling and convergence analysis of least squares polynomial Chaos regression , 2014, 1410.1931.
[112] W. J. Studden,et al. Theory Of Optimal Experiments , 1972 .