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Dmitrii Kochkov, Jamie Smith, Ayya Alieva, Mengqing Wang, Michael P. Brenner, Stephan Hoyer
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large-eddy simulation, our results are as accurate as baseline solvers with 8 to 10× finer resolution in each spatial dimension, resulting in 40- to 80-fold computational speedups. Our method remains stable during long simulations and generalizes to forcing functions and Reynolds numbers outside of the flows where it is trained, in contrast to black-box machine-learning approaches. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization.
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Kochkov, D., Smith, J., Alieva, A., Wang, M., Brenner, M. P., & Hoyer, S. (2021). Machine learning–accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences, 118(21). https://doi.org/10.1073/pnas.2101784118
Kochkov, Dmitrii, et al. "Machine learning–accelerated computational fluid dynamics." Proceedings of the National Academy of Sciences, vol. 118, no. 21, 2021, https://doi.org/10.1073/pnas.2101784118.
Kochkov, Dmitrii, Jamie Smith, Ayya Alieva, Mengqing Wang, Michael P. Brenner, and Stephan Hoyer. "Machine learning–accelerated computational fluid dynamics." Proceedings of the National Academy of Sciences 118, no. 21 (2021). https://doi.org/10.1073/pnas.2101784118.
Kochkov D., Smith J., Alieva A. [и др.] Machine learning–accelerated computational fluid dynamics // Proceedings of the National Academy of Sciences. — 2021. — Vol. 118, № 21. — URL: https://doi.org/10.1073/pnas.2101784118
@article{kochkov2021machine,
title = {{Machine learning–accelerated computational fluid dynamics}},
author = {Dmitrii Kochkov and Jamie Smith and Ayya Alieva and Mengqing Wang and Michael P. Brenner and Stephan Hoyer},
journal = {Proceedings of the National Academy of Sciences},
year = {2021},
volume = {118},
number = {21},
doi = {10.1073/pnas.2101784118},
url = {https://doi.org/10.1073/pnas.2101784118}
}TY - JOUR AU - Kochkov, Dmitrii AU - Smith, Jamie AU - Alieva, Ayya AU - Wang, Mengqing AU - Brenner, Michael P. AU - Hoyer, Stephan TI - Machine learning–accelerated computational fluid dynamics JO - Proceedings of the National Academy of Sciences VL - 118 IS - 21 PY - 2021 DA - 2021/05/18 DO - 10.1073/pnas.2101784118 UR - https://doi.org/10.1073/pnas.2101784118 ER -
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