We present a practical machine learning (ML) method for serving accessible nonlinear functions, which tackles a regression problem with tremendous parameters. By solving the modified Lugiato–Lefever equation, datasets for emulating the silicon-on-insulator platform and generating the on-chip optical frequency comb (OFC) are gathered. Furthermore, a feed-forward network-based ML model is used to train the datasets, and the prediction of the related parameters is implemented synchronously. Numerical results show that the model combining the finite element method with the ML technique is capable of predicting the properties of on-chip frequency combs for the first time, as far as we know, paving the way for analyzing OFCs based on integrated silicon photonics.

中文翻译：

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片上光频梳分析的机器学习回归方法

我们提出了一种实用的机器学习 (ML) 方法，用于为可访问的非线性函数提供服务，该方法解决了具有大量参数的回归问题。通过求解修改后的 Lugiato-Lefever 方程，收集了用于模拟绝缘体上硅平台和生成片上光频率梳 (OFC) 的数据集。此外，使用基于前馈网络的 ML 模型来训练数据集，并同步实现相关参数的预测。数值结果表明，有限元法与ML技术相结合的模型首次能够预测片上频率梳的特性，为基于集成硅光子分析OFCs铺平了道路。