Symbolic Regression

Symbolic regression aims to recover equations or other readable formulas from observations, helping researchers build models that can be inspected, tested, and extrapolated. Common approaches include evolutionary genetic programming, sparse identification methods such as SINDy, differentiable neural or tree-based models, and Bayesian searches that represent uncertainty over candidate expressions. Recent work also uses transformers and language-model agents to propose or guide searches, often combining these priors with numerical fitting and physical constraints; methods are tested on equation-recovery suites such as Feynman and SRBench as well as dynamical systems and PDE data. A central challenge is that good predictions do not guarantee recovery of the true law: noise, limited or uninformative measurements, and choices of search space can make distinct equations hard to distinguish, so robust evaluation and uncertainty estimates remain important.