Interpretable Scientific Discovery with Symbolic Regression: A Review

Understand

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.