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Photonics Research, Volume. 11, Issue 11, 1802(2023)

Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics

Ju Tang1、†, Ji Wu1、†, Jiawei Zhang1, Mengmeng Zhang1, Zhenbo Ren1,2,5、*, Jianglei Di1,3,6、*, Liusen Hu4, Guodong Liu4, and Jianlin Zhao1,7、*
Author Affiliations
  • 1Key Laboratory of Light Field Manipulation and Information Acquisition, Ministry of Industry and Information Technology, and Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710129, China
  • 2Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China
  • 3Key Laboratory of Photonic Technology for Integrated Sensing and Communication, Ministry of Education, and Guangdong Provincial Key Laboratory of Information Photonics Technology, Guangdong University of Technology, Guangzhou 510006, China
  • 4Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang 621900, China
  • 5e-mail: zbren@nwpu.edu.cn
  • 6e-mail: jiangleidi@gdut.edu.cn
  • 7e-mail: jlzhao@nwpu.edu.cn
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    Ju Tang, Ji Wu, Jiawei Zhang, Mengmeng Zhang, Zhenbo Ren, Jianglei Di, Liusen Hu, Guodong Liu, Jianlin Zhao, "Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics," Photonics Res. 11, 1802 (2023)

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    Paper Information

    Category: Instrumentation and Measurements

    Received: Jun. 13, 2023

    Accepted: Aug. 19, 2023

    Published Online: Oct. 7, 2023

    The Author Email: Zhenbo Ren (zbren@nwpu.edu.cn), Jianglei Di (jiangleidi@gdut.edu.cn), Jianlin Zhao (jlzhao@nwpu.edu.cn)

    DOI:10.1364/PRJ.497909

    Topics

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