Characteristic extraction of soliton dynamics based on convolutional autoencoder neural network
Congcong Liu, Jiangyong He, Pan Wang, Dengke Xing, Jin Li, Yange Liu, Zhi Wang
Abstract
In this article, we use a convolutional autoencoder neural network to reduce data dimensioning and rebuild soliton dynamics in a passively mode-locked fiber laser. Based on the particle characteristic in double solitons and triple solitons interactions, we found that there is a strict correspondence between the number of minimum compression parameters and the number of independent parameters of soliton interaction. This shows that our network effectively coarsens the high-dimensional data in nonlinear systems. Our work not only introduces new prospects for the laser self-optimization algorithm, but also brings new insights into the modeling of nonlinear systems and description of soliton interactions.