Interpretable Mathematical Expression

Interpretable mathematical expression discovery aims to automatically derive concise, understandable formulas from data, bridging the gap between complex data analysis and human understanding. Current research focuses on improving the efficiency and accuracy of algorithms like genetic programming, Monte Carlo Tree Search, and neural network-based approaches, often incorporating techniques like symbolic regression and pruning to find parsimonious solutions. These advancements are significant for scientific discovery, enabling the identification of underlying physical laws and improved model interpretability across diverse fields, from wind turbine modeling to cosmology. The resulting interpretable models enhance trust and facilitate deeper insights into complex systems.