Predicting nonlinear reshaping of periodic signals in optical fibre with a neural network
This work addresses the need for faster prediction of nonlinear optical effects in fiber systems, but it is incremental as it applies an existing neural network method to a specific physical scenario.
The authors tackled the problem of predicting nonlinear reshaping of periodic signals in optical fiber by deploying a supervised neural network model, achieving efficient probing of input parameters for custom comb generation and focusing effects.
We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second-order dispersion regimes of the fibre are studied, and the speed of the neural network is leveraged to probe the space of input parameters for the generation of custom combs or the occurrence of significant temporal or spectral focusing.