Symplectic Neural Networks for Learning Hamiltonian Dynamics

This paper develops a novel class of neural networks, termed symplectic neural networks, that inherently preserve the symplectic structure of Hamiltonian systems. By embedding symplectic principles directly into the network architecture, we achieve superior long-term stability and accuracy in learning complex physical dynamics from data, relevant for scientific machine learning.