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针对单模光纤中四波混频-受激拉曼散射(FWM-SRS)强非线性耦合效应难以解析的问题,本文提出了一种融合物理机理与神经网络的多尺度物理约束网络(Multi-Scale Physically Constrained Network,MSPC-Net)。该模型通过将非线性薛定谔方程(NLSE)的频域残差作为物理约束项嵌入网络优化过程,并设计多尺度空洞卷积模块融合局部细节、中程展宽及长程衰减特征,实现了光谱成分分离与物理参数的联合高精度反演。在250m与500m单模石英光纤实验中,MSPC-Net重建斯托克斯光谱的均方根误差(RMSE)分别低至0.014与0.0173,较传统卷积神经网络降低超68%;其频率偏移预测的平均绝对误差分别为0.03nm和0.04nm,精度较现有方法提升约90%。在信噪比(SNR)为6 dB的噪声环境下,MSPC-Net对FWM次峰信息的检测正确率高达95.3%,伪峰率低于4.7%。模型得益于物理约束的引导及轻量化结构设计,在SNR=15 dB噪声下RMSE增幅仅9.8%,并具备良好的实时处理能力,可部署于嵌入式设备,为高功率光通信系统优化与分布式光纤传感提供高效解决方案。本研究通过将严格物理规律与多尺度特征提取相结合,有效解决了长距离光纤复杂非线性效应的解析难题,显著提升了预测结果的理论符合度与噪声鲁棒性。Aiming at the challenging problem of analyzing the strong nonlinear coupling effect between four-wave mixing and stimulated Raman scattering in single-mode optical fibers, this paper introduces a novel multi-scale physically constrained network, designated as MSPC-Net, which effectively integrates fundamental physical mechanisms with advanced neural network techniques. The proposed model incorporates the frequency domain residual derived from the nonlinear Schrödinger equation directly into the network optimization procedure as a differentiable physical constraint term. This strategic inclusion ensures that the learning process remains consistent with the underlying physical principles governing light propagation in optical fibers. Furthermore, the model architecture employs a multiscale dilated convolution module specifically designed to capture and fuse features across different granularities, including fine local spectral details, intermediaterange broadening effects, and long-range attenuation trends. This multi-scale approach enables the simultaneous and high-precision inversion of both separated spectral components and critical physical parameters.
Experimental evaluations were conducted using single-mode quartz fibers with lengths of 250 meters and 500 meters. The results demonstrate that the Stokes spectra reconstructed by MSPC-Net achieve remarkably low root mean square errors, measuring only 0.014 and 0.0173 for the two fiber lengths respectively. This performance represents a reduction of more than sixty-eight percent compared to conventional convolutional neural networks. Additionally, the average absolute errors for frequency offset prediction are as low as 0.03 nanometer and 0.04 nanometer, corresponding to an accuracy improvement of approximately ninety percent relative to existing state-of-the-art methods. Under noisy conditions with a signal-to-noise ratio of 6 decibels, the model maintains an exceptional detection accuracy of up to 95.3 percent for identifying FWM sub-peak information, while keeping the pseudo-peak rate below 4.7 percent.
Benefiting from the strong guidance provided by embedded physical constraints and its lightweight structural design, the proposed model exhibits only a 9.8 percent increase in root mean square error even under challenging noise conditions with a signal-to-noise ratio of 15 decibels. Moreover, MSPC-Net demonstrates satisfactory real-time processing capabilities, making it suitable for deployment on embedded devices. This practical efficiency positions the model as a promising solution for optimizing high-power optical communication systems and advancing distributed optical fiber sensing applications. By successfully combining rigorous physical laws with multi-scale feature extraction, this research provides an effective approach to resolving the analytical difficulties associated with complex nonlinear effects in long-distance optical fibers, while significantly enhancing both the theoretical consistency and noise robustness of the prediction outcomes.-
Keywords:
- Nonlinear optics /
- Physically constrained neural networks /
- multi-scale feature extraction /
- spectral separation
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