用傅里叶特征网络高保真预测光纤受扰后的光场分布。
High-Fidelity Prediction of Perturbed Optical Fields using Fourier Feature Networks
- 将扰动编码为傅里叶特征，提升神经网络对高频光学响应的建模能力。
- 在实验数据上实现0.995的复相关系数，精度比标准网络提升一个量级。
- 适合需要少样本建模复杂光学系统的研究人员使用。
预测物理扰动对光信道的影响对先进光子器件至关重要，但现有建模方法通常计算成本高或需全面表征。本文提出一种数据高效机器学习框架，学习多模光纤中与扰动相关的传输矩阵。为克服高振荡函数建模难题，我们采用傅里叶特征基表示扰动，使紧凑的多层感知机能以高保真度学习映射关系。在压缩光纤的实验数据上，模型输出场与真实值的复相关系数达0.995，精度相比标准网络提升一个数量级，且参数量减少85%。该方法可从稀疏测量中建模复杂光学系统，具有普适性。
原文摘要 · Abstract (English)
Predicting the effects of physical perturbations on optical channels is critical for advanced photonic devices, but existing modelling techniques are often computationally intensive or require exhaustive characterisation. We present a novel data-efficient machine learning framework that learns the perturbation-dependent transmission matrix of a multimode fibre. To overcome the challenge of modelling the resulting highly oscillatory functions, we encode the perturbation into a Fourier Feature basis, enabling a compact multi-layer perceptron to learn the mapping with high fidelity. On experimental data from a compressed fibre, our model predicts the output field with a 0.995 complex correlation to the ground truth, improving accuracy by an order of magnitude over standard networks while using 85\% fewer parameters. This approach provides a general tool for modelling complex optical systems from sparse measurements.
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