You can discover hidden geometric symmetries in differential equations from data alone, enabling model reduction and better understanding of complex systems without needing closed-form equations.
This paper presents a machine learning method to automatically discover continuous symmetries (Lie point symmetries) directly from differential equation data. Rather than requiring symbolic equations, the approach uses operator theory on sampled data to identify geometric structures that can simplify models and improve scientific machine learning applications.