Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations.
@article{arxiv.2211.10873,
title = {Interpretable Scientific Discovery with Symbolic Regression: A Review},
author = {Nour Makke and Sanjay Chawla},
journal= {arXiv preprint arXiv:2211.10873},
year = {2025}
}