Physics-informed neural networks for deterministic modeling of polarization division multiplexed fiber transmission systems.

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

Abstract

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.

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。
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。
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