Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs

Abstract

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.

Figures (13)

• Figure 1: Parameterization of a NANF geometry with examples of nested nodes for two $\alpha$.
• Figure 2: For the initial design space of 18,188 designs: suppression ratio vs. fundamental mode confinement loss at 1400 nm.
• Figure 3: Classifier and regressor neural network structures for training/testing with dataset of $n$ designs.
• Figure 4: Classifier false positive and false negative rates vs. data set size.
• Figure 5: a) Mean absolute relative error vs. data set size $n$ for designs with $CL\leq$ 6.3 dB/km with error bars indicating standard deviation; b) estimated probability density function of the absolute relative error for $n$=1,819 and $n$=18,188.