Accurate and interpretable models are critical for understanding and interacting with complex systems. Although highly reliable, detailed physics models are themselves often complicated enough to defy straightforward analysis. A historically successful approach has been to identify limiting regimes in which the dynamics are determined by a dominant balance between terms in the model. We generalize this intuition by using recent model discovery and machine learning methods to search for distinct dynamical regimes in such systems.