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使用机器学习方法对槽波导特征进行参数优化
Yadvendra Singh1, Suraj Jena2, Harish Subbaraman3
1School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA. singhy@oregonstate.edu.
Scientific reports
|July 5, 2025
概括
机器学习准确地预测了插槽波导的性能. 随机森林模型有效地优化了集成光子学设计,增强了光学放大和切换应用.
科学领域:
- 综合光子学 综合光子学
- 计算电磁学的计算.
- 材料科学是一种材料科学.
背景情况:
- 插槽波导使低指数材料中的高电场限制成为可能,这对于集成光子设备至关重要.
- 它们的独特特性促进了与活性材料的相互作用,使光学放大和切换等应用成为可能.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测槽波导的功率限制和模式有效指数.
- 为了比较人工神经网络 (ANN),支持向量回归 (SVR) 和随机森林 (RF) 算法的性能,用于此预测任务.
主要方法:
- 使用有限元模拟来生成槽波导参数的训练数据.
- 三个ML算法 (ANN,SVR,RF) 被训练和测试以预测功率限制和模式有效指数.
- 使用平均绝对误差 (MAE),根平均平方误差 (RMSE),R平方和纳什-萨特克利夫效率 (NSE) 来评估性能.
主要成果:
- 随机森林 (RF) 模型表现出比ANN和SVR更高的性能.
- 对于各自的参数,RF实现了优异的精度,MAE为0.007和0.129,RMSE为0.054和0.185,R平方为0.961和0.998,NSE为0.960和0.998.
- 该研究验证了ML作为一个有效的方法来优化槽波导设计.
结论:
- 机器学习,特别是随机森林算法,为预测槽波导性能指标提供了有效和准确的方法.
- 这种基于机器学习的方法为先进的集成光子应用程序提供了对几何参数的高效优化.
- 开发的方法可以加速设计和开发新的槽波导结构.
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