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High Power Laser Science and Engineering, Volume. 10, Issue 2, 02000e12(2022)

Machine-learning guided optimization of laser pulses for direct-drive implosions

Fuyuan Wu1,2, Xiaohu Yang2,3, Yanyun Ma2,3, Qi Zhang1,2, Zhe Zhang4, Xiaohui Yuan1,2, Hao Liu1,2, Zhengdong Liu5, Jiayong Zhong2,5, Jian Zheng2,6, Yutong Li2,4, and Jie Zhang1,2、*
Author Affiliations
  • 1Key Laboratory for Laser Plasmas (MOE) and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai200240, China
  • 2Collaborative Innovation Center of IFSA, Shanghai Jiao Tong University, Shanghai200240, China
  • 3Department of Physics, National University of Defense Technology, Changsha410073, China
  • 4Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing100190, China
  • 5Department of Astronomy, Beijing Normal University, Beijing100875, China
  • 6Department of Plasma Physics and Fusion Engineering, University of Science and Technology of China, Hefei230026, China
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    Schematic of the DCI target structure and laser pulse to be optimized in this paper.

    Fig. 1. Schematic of the DCI target structure and laser pulse to be optimized in this paper.

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    Plasma implosion diagram and laser pulses obtained by (a) manual optimization and (b) machine-learning optimization.

    Fig. 2. Plasma implosion diagram and laser pulses obtained by (a) manual optimization and (b) machine-learning optimization.

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    (a) Typical laser pulses searched by the genetic algorithm and (b) the evolution of population fitness during the optimization.

    Fig. 3. (a) Typical laser pulses searched by the genetic algorithm and (b) the evolution of population fitness during the optimization.

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    (a) Feature importance obtained by the random forest algorithm and (b) scatter diagram of the ion temperature and the areal density for the last two rounds of optimization.

    Fig. 4. (a) Feature importance obtained by the random forest algorithm and (b) scatter diagram of the ion temperature and the areal density for the last two rounds of optimization.

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    (a) Typical laser pulse power in a cone and (b) double-cone target used in the 2021 DCI summer experiments.

    Fig. 5. (a) Typical laser pulse power in a cone and (b) double-cone target used in the 2021 DCI summer experiments.

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    • Table 1. Typical properties of manual optimization and machine-learning optimization.

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      Table 1. Typical properties of manual optimization and machine-learning optimization.

      PropertiesManual optimizationMachine learning optimization
      ρ (g/cm2)0.380.62
      Ti (keV)0.250.23
      V (km/s)233233
      IFAR3528
      Energy (kJ)2 × 62 × 5

    • Table 2. Comparison of the predicted and observed results in the DCI experimental campaign.

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      Table 2. Comparison of the predicted and observed results in the DCI experimental campaign.

      Properties2021 summer2021 summer2020 winter
      simulationsmeasurementsmeasurements
      Ti (eV)230200 ± 50165 ± 50
      Vimp (km/s)223210 ± 25135 ± 25
      tcost (ns)0.600.85 ± 0.21.19 ± 0.2
      ρ (g/cm2)0.620.20 ± 0.10.13 ± 0.08
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    Fuyuan Wu, Xiaohu Yang, Yanyun Ma, Qi Zhang, Zhe Zhang, Xiaohui Yuan, Hao Liu, Zhengdong Liu, Jiayong Zhong, Jian Zheng, Yutong Li, Jie Zhang. Machine-learning guided optimization of laser pulses for direct-drive implosions[J]. High Power Laser Science and Engineering, 2022, 10(2): 02000e12

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    Paper Information

    Category: Research Articles

    Received: Nov. 17, 2021

    Accepted: Jan. 24, 2022

    Posted: Jan. 25, 2022

    Published Online: Apr. 19, 2022

    The Author Email: Jie Zhang (jzhang1@sjtu.edu.cn)

    DOI:10.1017/hpl.2022.4

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology