对于C + L频段拉曼放大,采用物理信息的灰盒模型,包含发射功率配置文件
Optics express
|June 14, 2025
概括
对拉曼放大器的准确建模对于多频段系统至关重要. 这项研究引入了一种使用神经网络和线性回归的新型灰盒方法,显著减少对有效拉曼放大器建模的错误和数据需求.
科学领域:
- 光学工程是指光学工程.
- 计算物理 计算物理
- 电信 电信服务 电信服务 电信服务
背景情况:
- 拉曼放大器是多频段光学系统中至关重要的组件,提供广泛的增益配置和低噪声.
- 对拉曼放大器的准确建模对于系统设计和优化至关重要.
- 训练数据的稀缺性对纯数据驱动的神经网络模型构成重大挑战.
研究的目的:
- 为拉曼放大器开发一个数据高效的建模方案.
- 利用拉曼放大物理机制来提高模型的准确性和通用性.
- 为了减少拉曼放大器模型中的根平均平方误差 (RMSE).
主要方法:
- 一种灰框建模方法,结合了神经网络 (NN) 和线性回归 (LR).
- 一个基于NN的基模型在特定的发射功率条件下进行训练.
- 基本模型适应了使用LR的不同发射功率配置,并结合了物理洞察力.
主要成果:
- 模拟显示,使用5个拉曼的10THz频谱,RMSE减少了至少0.42dB.
- 与黑盒子方法相比,灰盒子模型显示出更高的概括性.
- 实验结果证实,与纯 NN 方法相比,RMSE 平均降低高达 0.79 dB.
结论:
- 拟议的灰盒方案有效地模拟了具有有限训练数据的拉曼放大器.
- 这种方法提高了模型的准确性和通用性,这对于实际的光学系统设计至关重要.
- 该方法比纯粹数据驱动的神经网络模型提供了显著的改进.
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