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
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Fig. 2. Schematic diagram of the structure of a multi-time-path CNN
Fig. 4. Comparison of reconstructed results with average pooling (
Fig. 5. Comparison of reconstructed results with maximum pooling (
Fig. 6. Comparison of the high-resolution reconstructed density data (weak nonlinear stage with ablation, disturbance wavelength=12 μm)
Fig. 7. Comparison of the high-resolution reconstructed density data (classical linear stage, disturbance wavelength=12 μm)
Fig. 8. Comparison of the high-resolution reconstructed density data (nonlinear stage with ablation, disturbance wavelength=12 μm)
Fig. 9. Comparison of the high-resolution reconstructed density data (weak nonlinear stage with ablation, disturbance wavelength=30 μm)
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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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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)
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