Multi-fidelity uncertainty propagation using polynomial chaos and Gaussian process modeling
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Jianmei Song | Shishi Chen | Fenfen Xiong | Fenggang Wang | Fenggang Wang | Jianmei Song | F. Xiong | Shishi Chen
[1] R. A. Miller,et al. Sequential kriging optimization using multiple-fidelity evaluations , 2006 .
[2] Wei Chen,et al. Nonhierarchical multi‐model fusion using spatial random processes , 2016 .
[3] Wei Chen,et al. Reduction of Epistemic Model Uncertainty in Simulation-Based Multidisciplinary Design , 2016 .
[4] Michael S. Eldred,et al. Multi-Fidelity Uncertainty Quantification: Application to a Vertical Axis Wind Turbine Under an Extreme Gust , 2014 .
[5] M. Eldred. Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design , 2009 .
[6] Pramudita Satria Palar,et al. Global sensitivity analysis via multi-fidelity polynomial chaos expansion , 2017, Reliab. Eng. Syst. Saf..
[7] Geoffrey T. Parks,et al. Decomposition-based Evolutionary Aerodynamic Robust Optimization with Multi-fidelity Point Collocation Non-intrusive Polynomial Chaos , 2015 .
[8] Jun Li,et al. Analysis of dataset selection for multi-fidelity surrogates for a turbine problem , 2018 .
[9] Bruno Sudret,et al. Adaptive sparse polynomial chaos expansion based on least angle regression , 2011, J. Comput. Phys..
[10] Claire Cannamela,et al. A Bayesian Approach for Global Sensitivity Analysis of (Multifidelity) Computer Codes , 2013, SIAM/ASA J. Uncertain. Quantification.
[11] Xiaoping Du,et al. Efficient Uncertainty Analysis Methods for Multidisciplinary Robust Design , 2002 .
[12] Slawomir Koziel,et al. Multi-Fidelity Robust Aerodynamic Design Optimization under Mixed Uncertainty , 2015 .
[13] Raphael T. Haftka,et al. Remarks on multi-fidelity surrogates , 2016, Structural and Multidisciplinary Optimization.
[14] Karen Willcox,et al. Fusing information from multifidelity computer models of physical systems , 2012, 2012 15th International Conference on Information Fusion.
[15] Liang Gao,et al. An important boundary sampling method for reliability-based design optimization using kriging model , 2015 .
[16] Sankaran Mahadevan,et al. Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis , 2018 .
[17] Stefan Görtz,et al. Alternative Cokriging Method for Variable-Fidelity Surrogate Modeling , 2012 .
[18] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[19] Yixin Liu,et al. Sequential optimization using multi-level cokriging and extended expected improvement criterion , 2018 .
[20] Loïc Le Gratiet,et al. Cokriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques for Multi-Fidelity Computer Codes , 2015, Technometrics.
[21] Yu Zhang,et al. Variable-fidelity expected improvement method for efficient global optimization of expensive functions , 2018 .
[22] Geoffrey T. Parks,et al. Corrigendum to “Multi-Fidelity Non-Intrusive Polynomial Chaos Based on Regression” [Comput. Methods Appl. Mech. Engrg. 305 (2016) 579–606] , 2016 .
[23] Peng Wang,et al. Kriging-based multi-fidelity optimization via information fusion with uncertainty , 2018, Journal of Mechanical Science and Technology.
[24] J. Wiart,et al. Polynomial-Chaos-based Kriging , 2015, 1502.03939.
[25] Joe Wiart,et al. A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry , 2015, J. Comput. Phys..
[26] Dongbin Xiu,et al. Computational Aspects of Stochastic Collocation with Multifidelity Models , 2014, SIAM/ASA J. Uncertain. Quantification.
[27] Claire Cannamela,et al. Kriging-based sequential design strategies using fast cross-validation techniques with extensions to multi-fidelity computer codes , 2012, 1210.6187.
[28] A. O'Hagan,et al. Predicting the output from a complex computer code when fast approximations are available , 2000 .
[29] Clyde Gumbert,et al. Multifidelity, Multidisciplinary Uncertainty Quantification with Non-Intrusive Polynomial Chaos , 2017 .
[30] Geoffrey T. Parks,et al. Multi-fidelity non-intrusive polynomial chaos based on regression , 2016 .
[31] Zhenghong Gao,et al. Research on multi-fidelity aerodynamic optimization methods , 2013 .
[32] A. Keane,et al. The development of a hybridized particle swarm for kriging hyperparameter tuning , 2011 .
[33] Andy J. Keane,et al. Efficient Multipoint Aerodynamic Design Optimization Via Cokriging , 2011 .
[34] Gerald Farin,et al. Curves and surfaces for computer aided geometric design , 1990 .
[35] Leo Wai-Tsun Ng,et al. Multifidelity Uncertainty Quantification Using Non-Intrusive Polynomial Chaos and Stochastic Collocation , 2012 .
[36] Dongbin Xiu,et al. Multi-fidelity stochastic collocation method for computation of statistical moments , 2017, J. Comput. Phys..
[37] Sang-Hoon Lee,et al. A comparative study of uncertainty propagation methods for black-box-type problems , 2008 .
[38] Ying Xiong,et al. Weighted stochastic response surface method considering sample weights , 2011 .
[39] Andrew S. Thelen,et al. Application of Multifidelity Optimization Techniques to Benchmark Aerodynamic Design Problems , 2016 .
[40] P. Sagaut,et al. Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging , 2008 .