High Power Laser and Particle Beams, Volume. 36, Issue 12, 122004(2024)

High-resolution reconstruction of the ablative RT instability flow field via convolutional neural networks

Zhiyang Xia1... Yuanyuan Kuang1,2, Yan Lu1,*, and Ming Yang13,* |Show fewer author(s)
Author Affiliations
  • 1School of Physics and Optoelectronic Engineering, Anhui University, Hefei 230601, China
  • 2School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
  • 3Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
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    References(26)

    [2] Yan Chao, Yu Jian, Xu Jinglei et al. On the achievements and prospects for the methods of computational fluid dynamics[J]. Adv Mech, 41, 562-589(2001).

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    [12] [12] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning f image recognition[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). 2016: 770778.

    [23] Zhong Baojiang, Lu Zhifang, Ji Jiahuan. Review on image interpolation techniques[J]. J Data Acquis Process, 31, 1083-1096(2016).

    [24] [24] Shi Wenzhe, Caballero J, Huszár F, et al. Realtime single image video superresolution using an efficient subpixel convolutional neural wk[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). 2016: 18741883.

    [26] [26] Kingma D P, Ba J. Adam: a method f stochastic optimization[C]Proceedings of the 3rd International Conference on Learning Representations. 2015.

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    Zhiyang Xia, Yuanyuan Kuang, Yan Lu, Ming Yang. High-resolution reconstruction of the ablative RT instability flow field via convolutional neural networks[J]. High Power Laser and Particle Beams, 2024, 36(12): 122004

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    Paper Information

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    Received: Feb. 25, 2024

    Accepted: Apr. 7, 2024

    Published Online: Jan. 15, 2025

    The Author Email: Lu Yan (luyan2003@ahu.edu.cn), Yang Ming (mingyang@ahu.edu.cn)

    DOI:10.11884/HPLPB202436.240015

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