arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2211.08985nlin.PSnlin.SIphysics.comp-phphysics.optics
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利用SCPINN预测optical fiber中光孤子的非线性动力学

Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN

Yin Fang, Wen-Bo Bo, Ru-Ru Wang, Yue-Yue Wang, Chao-Qing Dai

摘要:

通过在物理信息神经网络(PINN)的软约束中加入复合导数信息,提出了强约束物理信息神经网络(SCPINN)。该模型用于预测单模光纤中亮、暗皮秒光孤子和飞秒孤子分子的非线性动力学和形成过程,揭示孤子传输过程中脉冲能量、幅度、频谱和相位等物理量的变化规律。该神经网络引入自适应权值以加快损失函数的收敛速度。与PINN相比,SCPINN对孤子动力学的预测精度提高了5-11倍。因此,SCPINN是研究光纤中孤子动力学的一种具有前瞻性的方法。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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