通过模拟训练的深度神经网络进行连贯束组合的实验证明
Optics letters
|January 15, 2026
概括
我们展示了一个深度学习模型,用于连贯光束组合的相检索. 这种轻量级的模型,在模拟中训练,以最小的相位误差实现高效率,使得实验更快,更便携.
科学领域:
- 光学和光子学 在光学和光子学.
- 科学中的人工智能.
背景情况:
- 连贯束组合 (CBC) 对于高功率激光系统至关重要.
- 精确的相位控制对于高效的CBC至关重要,但传统方法可能很复杂.
- 瓦式光圈配置带来了独特的相位检索挑战.
研究的目的:
- 为了展示一种新的,模拟训练的深度学习方法,用于在7纤维放大器的光圈实验中检索相位.
- 评估深度学习辅助阶段控制的效率和准确性.
- 评估使用模拟训练的神经网络进行实验实施的实际优势.
主要方法:
- 开发和培训一个轻量级的深度学习模型,只使用模拟数据.
- 实时阶段控制的训练模型在连贯光束中实现,将实验与7个光纤放大器相结合.
- 使用深度学习辅助的阶段控制对剩余阶段误差的定量评估.
主要成果:
- 成功展示了用于阶段检索的深度学习模型的直接实施.
- 实现了高效的深度学习辅助相位控制,其余相位误差低于λ/30.
- 证明了快速训练 (<10分钟) 和先验优化能力.
结论:
- 模拟训练的神经网络在复杂的连贯束组合实验中提供了可行和高效的相检索解决方案.
- 深度学习方法简化了实验设置,并通过消除在培训期间需要采集实验数据来增强可移植性.
- 这种方法为更容易获得和高性能激光系统开发铺平了道路.
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