论文标题
通过SCPINN预测光纤中光孤子的非线性动力学
Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN
论文作者
论文摘要
通过将嵌入在物理知识的神经网络（PINN）软构成的化合物衍生物（PINN）中的化合物衍生物（PINN）的信息中添加，提出了强烈约束的物理知识神经网络（SCPINN）。它用于预测明亮和深色皮秒光学孤子的非线性动力学和形成过程，以及单模纤维中的飞秒孤子分子，并揭示了物理量的变化，包括能量，振幅，光谱和脉冲传输过程中脉冲的相位。引入自适应重量以加速这个新的神经网络中损耗函数的收敛性。与PINN相比，SCPINN在预测孤子动力学方面的准确性提高了5-11倍。因此，SCPINN是一种研究纤维中孤子动力学建模和分析的前瞻性方法。
The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the information of compound derivative embedded into the soft-constraint of physics-informed neural network(PINN). It is used to predict nonlinear dynamics and the formation process of bright and dark picosecond optical solitons, and femtosecond soliton molecule in the single-mode fiber, and reveal the variation of physical quantities including the energy, amplitude, spectrum and phase of pulses during the soliton transmission. The adaptive weight is introduced to accelerate the convergence of loss function in this new neural network. Compared with the PINN, the accuracy of SCPINN in predicting soliton dynamics is improved by 5-11 times. Therefore, the SCPINN is a forward-looking method to study the modeling and analysis of soliton dynamics in the fiber.
