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1.Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. (2014): Generative Adversarial Networks. 2014. 2.Tero Karras, Samuli Laine, Timo Aila. (2019): A Style-Based Generator Architecture for Generative Adversarial Networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4401-4410. 3.X. Guo, W. Li, F. Iorio. (2016): Convolutional neural networks for steady flow approximation, in: Proceedings of the 22nd ACM SIGKDD Interna- tional Conference on Knowledge Discovery and Data Mining, KDD 16, Association for Computing Machinery, 2016, pp. 481 - 490. 4.Julia Ling, Andrew Kurzawski, Jeremy Templeton. (2016): Reynolds Averaged Turbulence Modeling using Deep Neural Networks with Embedded Invariance, Journal of Fluid Mechanics, Volume 807, 2016, pp. 155 - 166. 5.Sangseung Lee, Donghyun You. (2017): Prediction of laminar vortex shedding over a cylinder using deep learning[J]. Journal of Fluid Mechanics, 2017. 6.Jonathan Long, Evan Shelhamer, Trevor Darrell. (2015): Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431-3440. 7.Olaf Ronneberger, Philipp Fischer, and Thomas Brox. (2015): U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234-241. 8.Mateus Dias Ribeiro, Abdul Rehman, Sheraz Ahmed, Andreas Dengel. (2020): DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks, 2020 9.Nils Thuerey, Konstantin Weissenow, Lukas Prantl, Xiangyu Hu. (2019): Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows, AIAA Journal, 2020. pp. 25 -36. 10.Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. (2018): UNet++: A Nested U-Net Architecture for Medical Image Segmentation, 2018. 11.Byungsoo Kim and Tobias Günther. (2019): Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks, Computer Graphics Forum June 2019. pp. 285 – 295. 12.Noriyasu Omata, and Susumu Shirayama. (2019): A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder, AIP Advances, 2019. 13.Enrico Fonda, Ambrish Pandeyb, Jorg Schumacher, and Katepalli R. Sreenivasan. (2019): Deep learning in turbulent convection networks, 2019. 14.M.S. Selig, University of Illinois at Urbana - Champaign. Aeronautical, and Astronautical En- gineering Department. UIUC Airfoil Data Site. Department of Aeronautical and Astronautical Engineering University of Illinois at Urbana- Champaign, 1996.
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