机器学习驱动下的光纤激光研究进展

耿翔; 赵春晓; 曹家宁; 李景玉; 吴函烁; 王鹏; 叶云; 奚小明; 张汉伟; 王小林

doi:10.11884/HPLPB202638.250284

机器学习驱动下的光纤激光研究进展

doi: 10.11884/HPLPB202638.250284

耿翔^{1, 2,},
赵春晓^{1, 2},
曹家宁¹,
李景玉¹,
吴函烁^{1, 2},
王鹏^{1, 2},
叶云^{1, 2},
奚小明^{1, 2},
张汉伟^{1, 2},
王小林^{1, 2, ,}

1.
国防科技大学前沿交叉学科学院，长沙 410073
2.
国防科技大学南湖之光实验室，长沙 410073

基金项目: 湖南省杰出青年基金(2023JJ10057); 国防科技大学自主创新科学基金(25-ZZCX-XXXJS-3)

详细信息

作者简介:
耿　翔，gx951008@163.com

通讯作者:
王小林，chinaphotonics@163.com。

中图分类号: O436
计量
- 文章访问数: 15
- HTML全文浏览量: 11
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2025-09-05
- 修回日期: 2025-12-30
- 录用日期: 2025-12-25
- 网络出版日期: 2026-01-17

Recent advances in machine learning-driven fiber lasers

Geng Xiang^{1, 2
,},
Zhao Chunxiao^{1, 2},
Cao Jianing¹,
Li Jingyu¹,
Wu Hanshuo^{1, 2},
Wang Peng^{1, 2},
Ye Yun^{1, 2},
Xi Xiaoming^{1, 2},
Zhang Hanwei^{1, 2},
Wang Xiaolin^{1, 2
, ,}

1.
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
2.
Nanhu Laser Laboratory, National University of Defense Technology, Changsha 410073, China

摘要

摘要: 光纤激光技术历经多年发展，已在现代工业与科研领域奠定了不可或缺的重要地位。然而，传统性能优化方法在效率、速度、精度等方面存在显著局限，难以满足高性能、高效率应用场景的需求。机器学习与光纤激光的深度融合为光纤激光系统的多维度性能优化提供了全新技术范式，显著提升了激光性能并拓展了技术边界。介绍了机器学习的分类、适用范围及应用场景，并综述了近年来机器学习在光纤激光器件设计、光纤激光器仿真与预测、激光器与输出特性智能控制，以及激光特性参数测量与表征中的研究现状。基于当前研究在数据依赖性、泛化能力、可解释性、计算效率等方面的技术挑战，展望了机器学习在光纤激光领域的发展趋势。
- 机器学习 /
- 光纤激光 /
- 神经网络 /
- 智能控制 /
- 性能优化
Abstract: Fiber laser technology, after years of development, has established an indispensable role in modern industrial and scientific research. However, traditional performance optimization methods have significant limitations in terms of efficiency, speed, and accuracy, making it difficult to meet the demands of high-performance and high-efficiency application scenarios. The deep integration of machine learning and fiber laser has provided a new technical paradigm for multidimensional performance optimization of fiber laser systems, significantly enhancing laser performance while expanding technological boundaries. This paper briefly introduces the classification of machine learning, applicable domains and corresponding application scenarios, with a focus on reviewing recent advances in laser device design, laser simulation and prediction, intelligent control of lasers and output characteristics, as well as the measurement and characterization of laser parameters. Based on the current technical challenges in data dependency, generalization ability, interpretability, and computational efficiency, future development trends of machine learning in fiber lasers are projected.
- machine learning /
- fiber lasers /
- neural network /
- intelligent control /
- performance optimization

HTML全文

图 1 多芯超模光纤放大器的增益均衡设计^[19]

Figure 1. Gain equalization design of multicore supermode fiber amplifier^[19]

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图 2 基于1D CNN-LSTM模型的PCF参数预测方法^[27]

Figure 2. Method for predicting PCF parameters based on the 1D CNN-LSTM model^[27]

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图 3 神经网络架构^[34]

Figure 3. Architecture of neural networks^[34]

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图 4 基于ANN的PPMFMOF前向设计方法^[37]

Figure 4. Forward design method of PPMFMOF based on ANN^[37]

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图 5 基于CNN-MLP模型的双波段滤波器逆向设计^[40]

Figure 5. Inverse design of the dual-band filter based on the CNN-MLP model^[40]

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图 6 基于机器学习的超宽带MSC设计^[41]

Figure 6. Machine learning assisted ultra-wideband MSC design^[41]

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图 7 DNN辅助的多跨段ROADM系统中的功率谱预测^[47]

Figure 7. DNN assisted optical power spectrum prediction in a multi-span ROADM system^[47]

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图 8 基于PINN的高阶孤子演化模型^[51]

Figure 8. PINN-based higher-order soliton evolution model^[51]

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图 9 基于WGAN-DNN的超连续谱预测模型^[54]

Figure 9. Supercontinuum prediction model based on a hybrid network with DNN and WGAN^[54]

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图 10 基于低延迟深度强化学习算法的超快光纤激光系统^[69]

Figure 10. Ultrafast fiber laser system based on the low-latency deep reinforcement learning algorithm^[69]

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图 11 结合螺旋相位调制的深度学习像差控制方法^[81]

Figure 11. Spiral phase modulation-assisted deep learning aberration control method^[81]

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图 12 基于脉冲拟合和自适应比特算法的脉冲时域整形^[86]

Figure 12. Pulse temporal shaping based on pulse fitting method and adaptive ratio algorithm^[86]

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图 13 基于BiLSTM-NN的非相干泵浦源光谱形状逆向设计^[88]

Figure 13. Inverse design of the incoherent pump source spectrum based on BiLSTM-NN^[88]

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图 14 基于两阶段相位控制方法生成OAM光束^[95]

Figure 14. OAM generation based on the two-stage phase control method^[95]

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图 15 PCGPA和DNN算法的阿秒脉冲重建结果对比^[108]

Figure 15. Reconstruction results of attosecond pulses with PCGPA and DNN model^[108]

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图 16 DeepMD的模式分解效果^[112]

Figure 16. Mode decomposition effects of DeepMD^[112]

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图 17 基于M²-Net的M²因子估计方法^[116]

Figure 17. M² factor estimation method based on the M²-Net^[116]

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