用少量数据训练模型，快速找到低损耗光纤设计。
Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs
- 分两阶段用神经网络筛选单模结构并预测损耗。
- 仅1819个样本即找出0.25 dB/km的超低损耗设计。
- 小数据可稳定预测，适合大规模光纤设计探索。
空芯光纤相比实芯结构具有更低损耗和更短延迟，但嵌套反谐振无节点光纤（NANFs）的几何复杂性使得传统优化计算成本过高。本文提出一种高效双阶段机器学习框架，仅需少量训练数据即可识别高性能NANF设计。模型首先使用神经网络（NN）分类器筛选单模设计（抑制比≥50 dB），再通过回归器预测光束缚损耗（CL）。通过在共 logarithm 损耗上训练，回归器克服了高动态范围难题。基于仅1,819个设计（均≥1 dB/km CL）的稀疏数据集，模型成功发现经验证为0.25 dB/km的优化设计，表明神经网络已捕捉到潜在物理规律并能外推至更低损耗区域。结果证明，小数据集即可实现稳定高精度预测，使对高达1400万种可能设计的探索成为可能，计算成本远低于有限元方法。
原文摘要 · Abstract (English)
Hollow-core fibers offer superior loss and latency characteristics compared to solid-core alternatives, yet the geometric complexity of nested antiresonance nodeless fibers (NANFs) makes traditional optimization computationally prohibitive. We propose a high-efficiency, two-stage machine learning framework designed to identify high-performance NANF designs using minimal training data. The model employs a neural network (NN) classifier to filter for single-mode designs (suppression ratio $\ge$ 50 dB), followed by a regressor that predicts confinement loss (CL). By training on the common logarithm of the loss, the regressor overcomes the challenges of high dynamic range. Using a sparse data set of only 1,819 designs, all with CL greater or equal to 1 dB/km, the model successfully identified optimized designs with a confirmed CL of 0.25 dB/km. {This demonstrates the NN has captured underlying physical behavior and is able to extrapolate to regions of lower CL. We show that small data sets are sufficient for stable, high-accuracy performance prediction, enabling the exploration of design spaces as large as $14e6$ cases at a negligible computational cost compared to finite element methods.
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