Volume 39, Number 6, December 2021
|Page(s)||1212 - 1221|
|Published online||21 March 2022|
- Ghoreyshi M, Jirasek A, Miller T, et al. Implementation and verification of gust modeling in an open-source flow solver[J]. Aerospace Science and Technology, 2019, 92(6): 777–789 [Article] [CrossRef] [Google Scholar]
- Qiao Lei, Bai Junqiang, Qiu Yasong, et al. High-efficiency solving method for steady transonic flow field[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(3): 470–479 [Article] (in Chinese) [Google Scholar]
- Ll Dali. Research on efficient implicit solver and many-core parallel optimization for structured high-order CFD[D]. Changsha: National University of Defense Technology, 2017 (in Chinese) [Google Scholar]
- Denil M, Shakibi B, Dinh L, et al. Predicting parameters in deep learning[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, 2013 [Google Scholar]
- Yang Wenhui. Neural network model compression methods based on parameters and features redundancy[D]. Xi'an: Xidian University, 2018 (in Chinese) [Google Scholar]
- Du J, Fang F, Pain C C, et al. POD reduced-order unstructured mesh modeling applied to 2D and 3D fluid flow[J]. Computers & Mathematics with Applications, 2013, 65(3): 362–379 [Article] [Google Scholar]
- John Burkardt, Max Gunzburger, Lee Hyungchun. POD and CVT-based reduced-order modeling of Navier-Stokes flows[J]. Computer Methods in Applied Mechanics and Engineering, 2006, 196(1): 337–355 [Article] [Google Scholar]
- Qiu Yasong, Bai Junqiang, Hua Jun. Flow field estimation method based on proper orthogonal decomposition and surrograte model[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(6): 1249–1260 [Article] (in Chinese) [Google Scholar]
- Wang Chen, Bai Junqiang, Jan Shehaven, et al. POD-Kriging reduced method's application in tandem cylinders' flow[J]. Journal of Northwestern Polytechnical University, 2018, 36(2): 220–228 [Article] [Article] (in Chinese) [CrossRef] [EDP Sciences] [Google Scholar]
- Chen Haixin, Deng Kaiwen, Li Runze. Utilization of machine learning technology in aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 522480 [Article] (in Chinese) [Google Scholar]
- Li K, Kou J, Zhang W. Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers[J]. Nonlinear Dynamics, 2019, 96(5): 1–21 [Article] [Google Scholar]
- Saleem W, Kharal A, Ahmad R, et al. Comparison of ACO and GA techniques to generate neural network based Bezier-Parsec parameterized airfoil[C]//The 2015 11th International Conference on Natural Computation, 2015 [Google Scholar]
- Zhu L, Zhang W, Kou J, et al. Machine learning methods for turbulence modeling in subsonic flows around airfoils[J]. Physics of Fluids, 2019, 31(1): 015105 [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Liu Changlong. From machine learning to deep learning: effective development based on scikit-learn and TensorFlow[M]. Beijing: Electronic Industry Press, 2019: 173–179 (in Chinese) [Google Scholar]
- Kim P, MATLAB deep learning: with machine learning neural networks and artificial intelligence[M]. Berkeley: Apress, 2017: 54–60 [Google Scholar]
- Liang R, Li T, Li L, et al. Knowledge consistency between neural networks and beyond[C]//International Conference on Learning Representations, Addis Ababa, 2019 [Google Scholar]
- Pan V Y, Chen Z Q. The complexity of the matrix eigenproblem[C]//Proceedings of the 31st Annual ACM Symposium on Theory of Computing, Atlanta, 1999 [Google Scholar]
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