半导体光学放大器的人工神经网络驱动的建模
Optics express
|August 13, 2025
概括
人工神经网络 (ANN) 模型有效模拟半导体光学放大器 (SOA). 这些模型使得超宽带传输系统中SOA的直接模拟和参数推理速度更快.
科学领域:
- 光电学是指光电子产品.
- 人工智能的人工智能
- 计算物理 计算物理
背景情况:
- 半导体光学放大器 (SOA) 是现代光通信系统的关键组件.
- 准确的SOA建模对于系统设计和优化至关重要.
- 现有的详细的SOA模型可能是计算密集的.
研究的目的:
- 开发人工神经网络 (ANN) 模型来模拟和参数化SOA.
- 为SOA创建高效的替代模型.
- 为了实现在线优化,使SOA的数字双胞胎表示更快.
主要方法:
- 开发多层前人工神经网络 (ANN) 模型.
- 实施一种直接方法,ANN可以复制详细的SOA模型的结果.
- 实施一个反向方法,ANN从输入输出数据中推断SOA参数.
主要成果:
- 在直接方法中,ANN模型的仿真速度比详细的SOA模型快20倍.
- 反向方法成功地使用ANN推断了SOA模型参数.
- 开发的ANN模型可以作为SOA的高效替代模型.
结论:
- ANNs为SOA模拟和参数化提供了一种计算效率高的方法.
- 开发的模型可以集成到数字双胞胎中,以实时优化光传输系统.
- 这项工作促进了利用SOA的超宽带传输系统的在线优化.
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