We present a Machine Learning approach based on Symbolic Regression to derive, from either numerically generated or experimentally measured spectral data, closed-form expressions that model the optical properties of biological materials. To evaluate the performance of our approach, we consider three case studies with the aim of retrieving the refractive index of the materials that constitute the biological structures considered. The results obtained show that, in addition to retrieving readable and dimensionally homogeneous dispersion models, the expressions found have a physical meaning and their algebraic form is similar to that of the models used to characterize the dispersive behavior of transparent dielectrics in the visible region.
@article{arxiv.2506.01862,
title = {Modeling the Optical Properties of Biological Structures using Symbolic Regression},
author = {Julian Sierra-Velez and Alexandre Vial and Marina Inchaussandague and Diana Skigin and Demetrio Macías},
journal= {arXiv preprint arXiv:2506.01862},
year = {2025}
}