Interpretable scientific discovery with symbolic regression: a review - Nour Makke (19160749) (2024)
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 tool, achieving significant advances in various application domains ranging from fundamental to applied sciences. In this survey, we present a structured and comprehensive overview of symbolic regression methods, review the adoption of these methods for model discovery in various areas, and assess their effectiveness. We have also grouped state-of-the-art symbolic regression applications in a categorized manner in a living review. Other Information Published in: Artificial Intelligence Review License: https://creativecommons.org/licenses/by/4.0 See article on publisher's website: https://dx.doi.org/10.1007/s10462-023-10622-0