突破数学极限——流体神经网络

Dana Mackenzie

工程(英文) ›› 2021, Vol. 7 ›› Issue (5) : 550-551.

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工程(英文) ›› 2021, Vol. 7 ›› Issue (5) : 550-551. DOI: 10.1016/j.eng.2021.03.009
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突破数学极限——流体神经网络

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Pushing Mathematical Limits, a Neural Network Learns Fluid Flow

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Dana Mackenzie. 突破数学极限——流体神经网络. Engineering. 2021, 7(5): 550-551 https://doi.org/10.1016/j.eng.2021.03.009

参考文献

[1]
Li Z, Kovachki N, Azizzadenesheli K, Liu B, Bhattacharya K, Stuart A, et al. Fourier neural operator for parametric partial differential equations. 2020. arXiv:2010.08895.
[2]
McCartney S. Eniac: the triumphs and tragedies of the world’s first computer. New York: Walker and Company; 1999.
[3]
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature 2017;550:354–9.
[4]
Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2014 Jun 23– 28; Columbus, OH, USA; 2014. p. 580–7.
[5]
Stakgold I, Holst M. Green’s functions and boundary value problems. 3rd ed. Hoboken: Wiley Interscience; 2011.
[6]
Frisch U. Turbulence: the legacy of A. N. Kolmogorov. Cambridge: Cambridge University Press; 1995.
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