Interpretable Scientific Discovery with Symbolic Regression: A Review
It provides a structured overview for researchers interested in interpretable machine learning in science, but it is incremental as it reviews existing methods without introducing new ones.
This review paper surveys symbolic regression methods, which learn interpretable mathematical expressions from data, and discusses their recent advances and applications across scientific domains.
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.