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Modeling of turbulent flows is still challenging.
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Kurth, T., Treichler, S., Romero, J., Mudigonda, M., Luehr, N., Phillips, E., Mahesh, A., Matheson, M., Deslippe, J., Fatica, M., Prabhat, Houston, M.: Exascale deep learning for climate analysis. Proceedings of The International Conference for High Performance Computing, Networking, Storage, and Analysis (2018)
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Bode, M., Collier, N., Bisetti, F., Pitsch, H.: Adaptive chemistry lookup tables for combustion simulations using optimal b-spline interpolants. Combustion Theory and Modelling 23
2019
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Gauding, M., Wang, L., Goebbert, J.H., Bode, M., Danaila, L., Varea, E.: On the self-similarity of line segments in decaying homogeneous isotropic turbulence. Computers & Fluids 180
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Lapeyre, C.J., Misdariis, A., Cazard, N., Veynante, D., Poinsot, T.: Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates. Combustion and Flame 203
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2019
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Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C.: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Lecture Notes in Computer Science 11133
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