Physics‐Informed Neural Network for Nonlinear Dynamics in Fiber Optics
Physics‐Informed Neural Network for Nonlinear Dynamics in Fiber Optics
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DOI：
10.1002/lpor.202100483
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发表时间：
2021-09
影响因子：
11
通讯作者：
Xiaotian Jiang;Danshi Wang;Qirui Fan;Min Zhang;Chao Lu;A. Lau
中科院分区：
文献类型：
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作者：
Xiaotian Jiang;Danshi Wang;Qirui Fan;Min Zhang;Chao Lu;A. Lau
研究了一种将深度学习与物理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，以学习光纤中的非线性动力学。对光纤中的色散、自相位调制和高阶非线性效应等多种物理效应进行了系统的研究和全面的验证。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。在现有的研究中，PINN主要是有效的单一场景。为了克服这个问题，物理参数（即，脉冲峰值功率和子脉冲的幅度）由此被嵌入作为附加的输入参数控制器，其允许PINN学习不同场景的物理约束并执行良好的概括性。此外，PINN使用更少的数据表现出比数据驱动神经网络更好的性能，并且其计算复杂度（就乘法次数而言）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，而且是推进光纤光学科学计算和自动建模的一种有前景的技术。
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. A systematic investigation and comprehensive verification on PINN for multiple physical effects in optical fibers is carried out, including dispersion, self‐phase modulation, and higher‐order nonlinear effects. Moreover, both the special case (soliton propagation) and general case (multipulse propagation) are investigated and realized with PINN. In existing studies, PINN is mainly effective for a single scenario. To overcome this problem, the physical parameters (i.e., pulse peak power and amplitudes of subpulses) 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 show that 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.