arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2109.00526physics.opticscs.LG
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纤维光学非线性动力学的物理信息神经网络

Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics

Beijing University of Posts and Telecommunications(北京邮电大学);The Hong Kong Polytechnic University(香港理工大学)

Xiaotian Jiang, Danshi Wang, Qirui Fan, Min Zhang, Chao Lu, Alan Pak Tao Lau

摘要:

研究了一种将深度学习与物理学相结合的物理信息神经网络(PINN),用于求解非线性薛定谔方程,以学习纤维光学中的非线性动力学。我们对PINN在光纤中的多种物理效应进行了系统的研究和全面的验证,包括色散、自相位调制和高阶非线性效应。此外,研究了特殊情况(孤子传输)和一般情况(多脉冲传输),并用PINN实现。在以往的研究中,PINN主要对单一场景有效。为了克服这个问题,物理参数(脉冲峰值功率和子脉冲的幅度)在此被嵌入作为额外的输入参数控制器,这允许PINN学习不同场景的物理约束并执行良好的泛化能力。此外,PINN表现出更好的性能比数据驱动的神经网络使用少得多的数据,其计算复杂度(乘法的数量)是远远低于分步傅立叶方法。结果表明,PINN不仅是一种有效的偏微分方程求解器,而且是一种有前途的技术,以促进科学计算和自动建模的纤维光学。

英文摘要:

A physics-informed neural network (PINN) that combines deep learning with physics is studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber optics. We carry out a systematic investigation and comprehensive verification on PINN for multiple physical effects in optical fibers, including dispersion, self-phase modulation, and higher-order nonlinear effects. Moreover, both special case (soliton propagation) and general case (multi-pulse propagation) are investigated and realized with PINN. In the previous studies, the PINN was mainly effective for single scenario. To overcome this problem, the physical parameters (pulse peak power and amplitudes of sub-pulses) are hereby embedded as additional input parameter controllers, which allow PINN to learn the physical constraints of different scenarios and perform good generalizability. Furthermore, PINN exhibits better performance than the data-driven neural network using much less data, and its computational complexity (in terms of number of multiplications) is much lower than that of the split-step Fourier method. The results report here show that the PINN is not only an effective partial differential equation solver, but also a prospective technique to advance the scientific computing and automatic modeling in fiber optics.

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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