Learning Reversible Symplectic Dynamics
Published in Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022
Time-reversal symmetry arises naturally as a structural property in many dynamical systems of interest. While the importance of hard-wiring symmetry is increasingly recognized in machine learning, to date this has eluded time-reversibility. In this paper, we propose a new neural network architecture for learning time-reversible dynamical systems from data. We focus in particular on an adaptation to symplectic systems, because of their importance in physics-informed learning. (oral)
Riccardo Valperga, Kevin Webster, Dmitry Turaev, Victoria Klein, Jeroen Lamb