Symbolic Regression

Symbolic regression (SR) is a machine learning technique aiming to discover concise, interpretable mathematical expressions that accurately model data. Current research emphasizes improving the efficiency and accuracy of SR algorithms, such as genetic programming and newer approaches incorporating neural networks and large language models, often focusing on techniques to handle noisy data and limited datasets. These advancements are significantly impacting scientific fields like materials science and physics by enabling the discovery of underlying physical laws and the development of more accurate and interpretable models for complex systems. The resulting interpretable models enhance scientific understanding and facilitate more efficient data analysis across various disciplines.