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The modeling of turbulent flows is critical to scientific and engineering problems ranging from aircraft design to weather forecasting and climate prediction.
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V Mnih, et al., Human-level control through deep reinforcement learning · 2015
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Y Zhiyin, Large-eddy simulation: Past, present and the future · 2015
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D Silver, et al., Mastering the game of go with deep neural networks and tree search · 2016
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S Levine, C Finn, T Darrell, P Abbeel, End-to-end training of deep visuomotor policies · 2016
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G Reddy, A Celani, TJ Sejnowski, M Vergassola, Learning to soar in turbulent environments · 2016
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J Ling, A Kurzawski, J Templeton, Reynolds averaged turbulence modelling using deep neural networks with embedded invariance · 2016
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K Duraisamy, G Iaccarino, H Xiao, Turbulence modeling in the age of data · 2019
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C Xie, J Wang, H Li, M Wan, S Chen, Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence · 2019
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A Beck, D Flad, CD Munz, Deep neural networks for data-driven les closure models · 2019
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P Garnier, et al., A review on deep reinforcement learning for fluid mechanics · 2019
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V Belus, et al., Exploiting locality and translational invariance to design effective deep reinforcement learning control of the 1-dimensional unstable falling liquid film · 2019
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L Biferale, F Bonaccorso, M Buzzicotti, P Clark Di Leoni, K Gustavsson, Zermelo’s problem: Optimal point-to-point navigation in 2d turbulent flows using reinforcement learning · 2019
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J Sirignano, JF MacArt, JB Freund, Dpm: A deep learning pde augmentation method with application to large-eddy simulation · 2020
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