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
The amplifying dynamics of the pulse burst in Yb^(3+)-doped fiber amplifier(YDFA)with high-power pulse pump is numerically analyzed by a finite-difference time-domain(FDTD)method.The numerical simulations show that th...The amplifying dynamics of the pulse burst in Yb^(3+)-doped fiber amplifier(YDFA)with high-power pulse pump is numerically analyzed by a finite-difference time-domain(FDTD)method.The numerical simulations show that the amplitude uniformity of the amplified pulse burst can be modified by adjusting the parameters of pump,such as relative delay and power.Though optimizing the pump parameters,we can reduce the gain difference between the pulses in a burst and improve the efficiency of coherent pulse stacking based on Gires-Tournois interferometers(GTIs).These results can be applied to the design of high energy ultra-short pulse amplifiers based on burst-mode amplification and coherent pulse stacking technology.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.
基金supported by the National Key Research and Development Program of China(No.2018YFB0504400)the National Natural Science Foundation of China(Nos.61775107,11674177,61640408)the Tianjin Natural Science Foundation(No.19JCZDJC31200)。
文摘The amplifying dynamics of the pulse burst in Yb^(3+)-doped fiber amplifier(YDFA)with high-power pulse pump is numerically analyzed by a finite-difference time-domain(FDTD)method.The numerical simulations show that the amplitude uniformity of the amplified pulse burst can be modified by adjusting the parameters of pump,such as relative delay and power.Though optimizing the pump parameters,we can reduce the gain difference between the pulses in a burst and improve the efficiency of coherent pulse stacking based on Gires-Tournois interferometers(GTIs).These results can be applied to the design of high energy ultra-short pulse amplifiers based on burst-mode amplification and coherent pulse stacking technology.
