Active Learning in Symbolic Regression with Physical Constraints

A variety of established methods exist for modeling data, ranging from traditional machine learning techniques (linear regression, ridge regression, polynomial regression) to deep learning approaches (neural networks). However, these methods suffer from constraints and/or interpretability, such as limiting the model to a particular shape (e.g., linear), or being too complex to interpret (black box models). Symbolic Regression (SR) is less constrained and searches through the mathematical space of equations. SR allows for discovering a broader range of functional relationships, including those with nonlinear or intricate interactions between variables.