基于循环神经网络的超短脉冲光纤放大器模型(特邀)

Ultra-short pulse fiber amplifier model based on recurrent neural network(Invited)

针对超短脉冲光纤放大器模型复杂,计算难度大等问题,提出了一种基于门控循环单元深度学习的脉冲演化预测方法。利用初始脉冲时域和频域信息,分别训练门控循环单元模型,成功地预测了掺铥光纤放大器中脉冲非线性压缩的过程,与数值计算和实验结果匹配。相比于求解非线性薛定谔方程和能级速率方程两个偏微分方程的方法具有更高的运算速度,有利于优化放大器参数,理解超短脉冲在增益光纤中的非线性动力学过程。

Aiming at the problems of complex model and difficult calculation of ultra-short pulse fiber amplifier,a pulse evolution prediction method based on deep learning of gated recurrent unit was proposed.The gate recurrent unit model was trained respectively based on the initial pulse information in the time domain and frequency domain.One nonlinear pulse compression process in thulium-doped fiber amplifier is successfully predicted,which matched the numerical calculation and experimental results.Compared with solving the nonlinear Schr?dinger equation and the rate equation,this method has higher operation speed,which is beneficial to optimize the amplifier parameters and understand the nonlinear dynamic process of ultra-short pulses in the gain fiber.