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

Author:

Sui HaoORCID,Zhu HongnaORCID,Jia Huanyu,Li Qi,Ou Mingyu1,Luo Bin1,Zou Xihua1ORCID,Yan Lianshan1ORCID

Affiliation:

1. Southwest Jiaotong University

Abstract

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.

Funder

National Key Research and Development Program of China

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics

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