A hybrid network with DNN and WGAN for supercontinum prediction

Abstract

In this paper, a Hybrid Network is reported for predicting the output pulse shape parameters of the supercontinuum in optical fibers. The network incorporates the Wasserstein generative adversarial network (WGAN) and the deep neural network (DNN) with a TCN layer. The WGAN augments a limited set of original data, while the designed DNN accurately predicts supercontinuum. The simulation results show the spectra agreement (Agm) of the predictions by individual DNN is 89% and the root mean square error (RMSE) is 0.1092. After retraining with WGAN's extended dataset, the Agm of DNN predictions is improved to 98% and the RMSE is reduced to 0.0513. The input pump power corresponding to the appearance of the fourth soliton peak can be predicted by the retrained DNN. Numerical analysis demonstrates the effectiveness and generalization ability of a Hybrid Network in predicting pulse parameters. The network fits the propagation dynamics in the spectral domain of SC, which provides a promising tool for further research in this field.