Symbolic Density Estimators for Unnormalized Distributions
The framework combines deep generative models with Symbolic Regression to discover explicit mathematical forms of probability density functions from observed data. It uses flow-based and score-based models to learn samples, and then fits a symbolic expression using evolutionary or neural network methods. This allows for the automated discovery of interpretable physical relationships, such as Hamiltonian functions, from complex datasets.