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Min Jiang,  Hanshuo Wu,  Yi An,  Tianyue Hou,  Qi Chang,  Liangjin Huang,  Jun Li,  Rongtao Su,  Pu Zhou

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Min Jiang, Hanshuo Wu, Yi An, Tianyue Hou, Qi Chang, Liangjin Huang, Jun Li, Rongtao Su, Pu Zhou. Fiber laser development enabled by machine learning: review and prospect[J]. PhotoniX. doi: 10.1186/s43074-022-00055-3
Citation: Min Jiang, Hanshuo Wu, Yi An, Tianyue Hou, Qi Chang, Liangjin Huang, Jun Li, Rongtao Su, Pu Zhou. Fiber laser development enabled by machine learning: review and prospect[J]. PhotoniX. doi: 10.1186/s43074-022-00055-3
shu

doi: 10.1186/s43074-022-00055-3

Fiber laser development enabled by machine learning: review and prospect

  • College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, 410073, China

Funds: This work is supported by Projects for National Excellent Young Talents and Hunan Provincial Innovation Construct Project (No. 2019RS3017).

    摘要
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    Abstract: In recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers, in particular complex, dynamical, or disturbance-sensitive fiber laser systems. This paper highlights recent attractive research that adopted machine learning in the fiber laser field, including design and manipulation for on-demand laser output, prediction and control of nonlinear effects, reconstruction and evaluation of laser properties, as well as robust control for lasers and laser systems. We also comment on the challenges and potential future development.
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    • 参考文献(168)
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  • 收稿日期:  2022-01-08
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