We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data. Our approach uses <10\% of experimental data needed for best performance and outperforms analytical models for a Mach-Zehnder interferometer mesh.
@article{arxiv.2211.16038,
title = {Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning},
author = {Ali Cem and Ognjen Jovanovic and Siqi Yan and Yunhong Ding and Darko Zibar and Francesco Da Ros},
journal= {arXiv preprint arXiv:2211.16038},
year = {2022}
}