We present a practical machine learning (ML) method for serving accessible non-linear 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.
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