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Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence.
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2000
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2001
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F. Sarghini, G. de Felice, and S. Santini, “Neural Networks Based Subgrid Scale Modeling in Large Eddy Simulations,” Comput Fluids 32
2003
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2004
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P. Sagaut, Large eddy simulation for incompressible flows: an introduction , 3rd ed., Scientific computation (Springer, Berlin ; New York, 2006)
2006
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H. Pitsch, “Large-Eddy Simulation of Turbulent Combustion,” Annu. Rev. Fluid Mech. 38
2006
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S. Hickel, N. A. Adams, and J. A. Domaradzki, “An adaptive local deconvolution method for implicit LES,” J. Comput. Phys. 213
2006
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2006
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M.-A. Habisreutinger, R. Bouffanais, E. Leriche, and M. O. Deville, “A coupled approximate deconvolution and dynamic mixed scale model for large-eddy simulation,” J. Comput. Phys. (2007), 10.1016/j.jcp.2007.02.010
J.-X. Wang, J.-L. Wu, and H. Xiao, “Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data,” Phys. Rev. Fluids 2
2017
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M. Gamahara and Y. Hattori, “Searching for Turbulence Models by Artificial Neural Network,” Phys. Rev. Fluids 2
2017
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R. Maulik and O. San, “A Neural Network Approach for the Blind Deconvolution of Turbulent Flows,” J. Fluid Mech. 831
2017
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2017
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2017
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2007
Cited alongside, same era.
T. Ishihara, Y. Kaneda, M. Yokokawa, K. Itakura, and A. Uno, “Small-scale statistics in high-resolution direct numerical simulation of turbulence: Reynolds number dependence of one-point velocity gradient statistics,” J. Fluid Mech. 592
2007
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C. Fureby, “Towards the use of large eddy simulation in engineering,” Progress in Aerospace Sciences 44
2008
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Y. Shi, Z. Xiao, and S. Chen, “Constrained subgrid-scale stress model for large eddy simulation,” Phys. Fluids 20
2008
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E. Garnier, N. Adams, and P. Sagaut, Large Eddy Simulation for Compressible Flows , Scientific Computation (Springer Netherlands, Dordrecht, 2009)
2009
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T. Ishihara, T. Gotoh, and Y. Kaneda, “Study of High–Reynolds Number Isotropic Turbulence by Direct Numerical Simulation,” Annu. Rev. Fluid Mech. 41
2009
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N. J. Georgiadis, D. P. Rizzetta, and C. Fureby, “Large-Eddy Simulation: Current Capabilities, Recommended Practices, and Future Research,” AIAA Journal 48
2010
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C. Meneveau, “Lagrangian Dynamics and Models of the Velocity Gradient Tensor in Turbulent Flows,” Annu. Rev. Fluid Mech. 43
2011
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P. Sagaut and C. Cambon, Homogeneous Turbulence Dynamics (Springer International Publishing, Cham, 2018)
2018
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P. A. Durbin, “Some Recent Developments in Turbulence Closure Modeling,” Annu. Rev. Fluid Mech. 50
2018
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2018
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S. Pan and K. Duraisamy, “Long-time predictive modeling of nonlinear dynamical systems using neural networks,” Complexity 2018
2018
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C. Ma, J. Wang, and W. E, “Model Reduction with Memory and the Machine Learning of Dynamical Systems,” CiCP 25
2018
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R. Maulik, O. San, A. Rasheed, and P. Vedula, “Data-driven deconvolution for large eddy simulations of Kraichnan turbulence,” Phys. Fluids 30
2018
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C. Xie, J. Wang, H. Li, M. Wan, and S. Chen, “A modified optimal LES model for highly compressible isotropic turbulence,” Phys. Fluids 30
2018
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L. Zhu, W. Zhang, J. Kou, and Y. Liu, “Machine learning methods for turbulence modeling in subsonic flows around airfoils,” Phys. Fluids 31
2019
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P. A. Srinivasan, L. Guastoni, H. Azizpour, P. Schlatter, and R. Vinuesa, “Predictions of Turbulent Shear Flows Using Deep Neural Networks,” Phys. Rev. Fluids 4
2019
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Z. Zhou, G. He, S. Wang, and G. Jin, “Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network,” Comput. Fluids 195
2019
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M. Raissi, H. Babaee, and P. Givi, “Deep Learning of Turbulent Scalar Mixing,” Phys. Rev. Fluids 4
2019
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A. Beck, D. Flad, and C.-D. Munz, “Deep Neural Networks for Data-Driven LES Closure Models,” J. Comput. Phys. 398
2019
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X. I. A. Yang, S. Zafar, J.-X. Wang, and H. Xiao, “Predictive large-eddy-simulation wall modeling via physics-informed neural networks,” Phys. Rev. Fluids 4
2019
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K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata, “Synthetic turbulent inflow generator using machine learning,” Phys. Rev. Fluids 4
2019
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K. Duraisamy, G. Iaccarino, and H. Xiao, “Turbulence Modeling in the Age of Data,” Annu. Rev. Fluid Mech. 51
2019
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C. Xie, J. Wang, and W. E, “Modeling Subgrid-Scale Forces by Spatial Artificial Neural Networks in Large Eddy Simulation of Turbulence,” Phys. Rev. Fluids 5
2020
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2020
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2020
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