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支持强化学习的强有力的阶段控制用于OAM光束生成在连贯光束组合系统中的光束
Optics express
|February 20, 2026
概括
本研究介绍了一种强化学习框架,用于精确控制激光系统中的轨道角动量 (OAM) 束. 人工智能实现了高纯度的OAM光束生成,优于传统方法.
科学领域:
- 光学和光子学 在光学和光子学.
- 激光物理 激光物理
- 科学中的人工智能.
背景情况:
- 轨道角动量 (OAM) 束对于先进的应用至关重要.
- 高功率的OAM发电面临着模式竞争和低损坏值等挑战.
- 一致光束组合 (CBC) 提供了一个解决方案,但精确的相位控制是困难的.
研究的目的:
- 开发一个智能,无标签,高精度的阶段控制框架,用于OAM光束生成在CBC系统.
- 为了利用强化学习 (RL) 实现稳健高效的OAM光束合成.
主要方法:
- 实施基于强化学习的阶段控制框架.
- 整合了一个基于物理信息的奖励功能.
- 在演员和批评网络中集成矢量量化 (VQ) 模块.
- 适用于OAM光束生成的12通道CBC系统.
主要成果:
- 快速而强大的单阶段生成±1和±2OAM光束.
- 即使在杂的环境中,也可以达到超过0.99的模式纯度.
- 与随机平行梯度下降 (SPGD) 算法相比,证明了优越的控制稳定性.
- 在明显低于SPGD的控制频率下有效运行.
结论:
- 拟议的RL框架允许在CBC系统中对OAM光束产生先进的相位控制.
- 这种方法为智能光场调制提供了一个可扩展和物理接地范式.
- 这些发现推动了RL在连贯光束组合和光学系统中的应用.
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