Interpretable Scientific Discovery with Symbolic Regression: A Review

TL;DR

This paper reviews the recent advances in symbolic regression, highlighting its role in interpretable scientific discovery through both genetic programming and deep learning approaches.

Contribution

It provides a comprehensive overview of symbolic regression methods, comparing traditional genetic programming with modern deep learning techniques, and discusses their respective strengths and limitations.

Findings

Deep learning approaches have significantly advanced symbolic regression capabilities.

Symbolic regression enables interpretable model discovery directly from data.

The survey highlights the strengths and limitations of current methods.

Abstract

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.