强激光与粒子束2026，Vol.38Issue(5)：38-59,22.DOI:10.11884/HPLPB202638.250284
机器学习驱动下的光纤激光研究进展
Recent advances in machine learning-driven fiber lasers
摘要
Abstract
Machine learning(ML)has emerged as a transformative approach for advancing fiber laser technology,offering powerful solutions to overcome the limitations of traditional design,optimization,and control methods.This review systematically examines the integration of ML across the entire fiber laser ecosystem.It begins by categorizing fundamental ML paradigms,with a discussion of their respective applicability.The subsequent sections detail recent research progress in key areas including intelligent device design,which encompasses ML-assisted optimization of doped fibers,photonic crystal fibers,anti-resonant fibers,polarization-maintaining fibers,fiber gratings,and mode-selective couplers;laser simulation and prediction,focusing on models for power,temporal dynamics,and spectral evolution;intelligent control of laser output,covering adaptive mode-locking,coherent beam combining,and spatiotemporal pulse shaping;and laser characterization,highlighting ML-enhanced techniques for temporal pulse measurement,mode decomposition,and beam quality evaluation.The review further addresses prevailing challenges such as data dependency,model generalizability,interpretability,and computational efficiency,while outlining future directions toward developing more robust,efficient,and physically interpretable ML-driven fiber laser systems.关键词
机器学习/光纤激光/神经网络/智能控制/性能优化Key words
machine learning/fiber lasers/neural network/intelligent control/performance optimization分类
数理科学引用本文复制引用
耿翔,王小林,赵春晓,曹家宁,李景玉,吴函烁,王鹏,叶云,奚小明,张汉伟..机器学习驱动下的光纤激光研究进展[J].强激光与粒子束,2026,38(5):38-59,22.基金项目
湖南省杰出青年基金(2023JJ10057) （2023JJ10057）
国防科技大学自主创新科学基金(25-ZZCX-XXXJS-3) （25-ZZCX-XXXJS-3）
