Junyu Shen

Discovering Multiple Conservation Laws from Trajectories by Machine Learning

The proposed OAC-Net provides an effective framework for discovering multiple interpretable integrals of motion from complex trajectory data and proves that gradient orthogonality of real-analytic functions implies the functional independence of the learned integrals of motion.