Predicting nonlinear multi-pulse propagation in optical fibers via a lightweight convolutional neural network

The nonlinear evolution of ultrashort pulses in optical fiber has broad applications, but the computational burden of convolutional numerical solutions necessitates rapid modeling methods. Here, a lightweight convolutional neural network is designed to characterize nonlinear multi-pulse propagation in highly nonlinear fiber. With the proposed network, we achieve the forward mapping of multi-pulse propagation using the initial multi-pulse temporal profile as well as the inverse mapping of the initial multi-pulse based on the propagated multi-pulse with the coexistence of group velocity dispersion and self-phase modulation. A multi-pulse comprising various Gaussian pulses in 4-level pulse amplitude modulation is utilized to simulate the evolution of a complex random multi-pulse and investigate the prediction precision of two tasks. The results obtained from the unlearned testing sets demonstrate excellent generalization and prediction performance, with a maximum absolute error of 0.026 and 0.01 in the forward and inverse mapping, respectively. The approach provides considerable potential for modeling and predicting the evolution of an arbitrary complex multi-pulse.