深度学习在波前重构与波前预测中的研究进展  

Research progress in deep learning for wavefront reconstruction and wavefront prediction

作　　者：邱从攀 刘国栋[1] 张大勇[1] 胡流森[1] Qiu Congpan;Liu Guodong;Zhang Dayong;Hu Liusen(Institute of Fluid Physics,CAEP,Mianyang 621900,China)

机构地区：[1]中国工程物理研究院流体物理研究所,四川绵阳621900

出　　处：《强激光与粒子束》2024年第7期4-13,共10页High Power Laser and Particle Beams

摘　　要：深度学习技术与自适应光学技术的结合,预期能够有效提升波前校正效果,并能更好地应对更复杂的环境条件。详细梳理了在波前重构技术和波前预测技术方向上应用深度学习的研究进展,包括研究者在这两个研究方向中所采用的具体研究方法以及相应的神经网络结构设计,同时分析了这些神经网络在不同实际应用场景下的性能表现,并对不同神经网络结构之间的差异进行了比较和讨论,探究了结构差异所带来的具体影响。最后,总结了深度学习在这两个方向上的已有方法,并就未来深度学习与自适应光学技术如何深度融合的发展趋势进行了展望。The combination of deep learning technology and adaptive optics technology is expected to effectively improve the wavefront correction effect and better cope with more complex environmental conditions.The research progress of applying deep learning in the direction of wavefront reconstruction and wavefront prediction is detailed,including the specific research methods and corresponding neural network structure design adopted by the researchers in these two research directions.The performance of these neural networks in different practical application scenarios is analyzed,the differences between different neural network structures are compared and discussed,and the specific impacts of the structural differences are explored.Finally,the existing methods of deep learning in these two directions are summarized,and the future development trend of the deep integration of deep learning and adaptive optics technology is also prospected.

关 键 词：深度学习 自适应光学 波前重构 波前预测 

分 类 号：O43[机械工程—光学工程]