Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning

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.