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...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.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12274238 and 61835006)the National Key Research and Development Program of China(No.2018YFB1801802)+2 种基金the Beijing-Tianjin-Hebei Basic Research Cooperation Project(No.21JCZXJC00010)the Natural Science Foundation of Tianjin City(No.19JCZDJC31200)the Tianjin Research Innovation Project for Postgraduate Students(No.2021YJSB083)。
文摘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.
