# Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs

arXiv: https://arxiv.org/abs/2603.13302 · HTML: https://mlsanity.com/paper/2603.13302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [Inverse design of photonic devices with strict foundry fabrication constraints](https://mlsanity.com/paper/2201.12965/) (2022)
- [Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers](https://mlsanity.com/paper/2309.15747/) (2023)

## Abstract (arXiv)

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 ≥ 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.