基于物理信息的偏振分复用光纤传输系统确定性建模神经网络。

Applied optics Pub Date : 2025-09-10 DOI:10.1364/AO.571796
Shihong Xu, Xinyi Xu, Run Zhou, Jiahao Zhang, Qun Zhang, Lu Zhang
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引用次数: 0

摘要

耦合非线性Schrödinger方程(CNLSE)控制着偏振分复用(PDM)光纤系统中的信号传播,但在数值计算上存在重大挑战。本文介绍了物理信息神经网络(pinn)作为PDM传输确定性建模的新框架。通过对单脉冲演化、通信序列和全PDM系统的验证,pinn在克服传统限制的同时表现出确定性精度(RMSE=0.0044 ~ 0.0129,光谱误差%)。它们消除了分步傅里叶方法(SSFM)的步长依赖性和数据驱动方法的统计不确定性。据我们所知,通过嵌入PDE约束来保持物理确定性,pinn为可靠的光纤系统建模建立了一个新的范例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physics-informed neural networks for deterministic modeling of polarization division multiplexed fiber transmission systems.

The coupled nonlinear Schrödinger equation (CNLSE) governs signal propagation in polarization division multiplexed (PDM) optical fiber systems, yet poses significant numerical challenges. This paper introduces physics-informed neural networks (PINNs) as a novel framework for deterministic modeling of PDM transmission. Through validation across single-pulse evolution, communication sequences, and full PDM systems, PINNs demonstrate deterministic accuracy (RMSE=0.0044∼0.0129 and spectralerrors<4%) while overcoming traditional limitation. They eliminate the split-step Fourier method (SSFM)'s step-size dependencies and data-driven methods' statistical uncertainties. By preserving physical determinism through embedded PDE constraints, PINNs establish a new paradigm, to our knowledge, for reliable fiber-optic system modeling.

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