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Updated: Jan 17, 2026

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基于物理学的神经网络用于极化分裂多重复合光纤传输系统的确定性建模

Shihong Xu, Xinyi Xu, Run Zhou

|September 22, 2025

概括

基于物理学的神经网络 (PINNs) 提供了一个确定性的解决方案,用于模拟由合非线性施罗丁格方程 (CNLSE) 规范的光纤系统. 这种新的方法克服了传统方法的局限性,确保了准确的信号传播分析.

科学领域:

光学通信工程光学通信工程
计算物理计算物理
在工程领域的人工智能.

背景情况:

合非线性施罗丁格方程 (CNLSE) 对于在极化分裂多重 (PDM) 光纤系统中模拟信号传播至关重要.
传统的数值方法,如分步里叶法 (SSFM) 面临的挑战是步骤大小的依赖.
数据驱动的方法引入了统计不确定性,限制了决定性建模.

研究的目的:

引入物理信息神经网络 (PINNs) 作为PDM光纤系统确定性建模的新型框架.
为了证明PINNs在模拟信号传播中的准确性和可靠性.
克服现有的数值和数据驱动方法的局限性.

主要方法:

通过将 CNLSE 嵌入为物理约束来实现基于物理的神经网络 (PINNs).
在各种场景中验证PINNs:单脉冲演变,通信序列和完整的PDM系统.
将PINN的性能与SSFM等传统方法进行比较.

主要成果:

PINNs实现了决定性准确性,其平方根平均误差 (RMSE) 在0.0044和0.0129.0之间.
频谱误差始终低于4%,表明建模中的高保真性.
PINNs消除了SSFM固有的阶段大小依赖性和数据驱动方法的统计不确定性.

结论:

PINNs为PDM光纤系统提供了可靠和决定性的建模范式.
这种方法保留了物理决定性,为信号传播分析提供了新的标准.
PINN代表了光通信系统计算建模的重大进步.

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