Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian
This work addresses the problem of accurate and interpretable astronomical simulations for researchers, though it appears incremental as it builds on existing Hamiltonian methods by incorporating Lie groups.
The paper tackled the challenge of modeling N-body celestial interactions with rotations and multiscale dynamics by extending Hamiltonian-based deep learning to Lie group manifolds, resulting in improved training stability and prediction accuracy.
Incorporating the Hamiltonian structure of physical dynamics into deep learning models provides a powerful way to improve the interpretability and prediction accuracy. While previous works are mostly limited to the Euclidean spaces, their extension to the Lie group manifold is needed when rotations form a key component of the dynamics, such as the higher-order physics beyond simple point-mass dynamics for N-body celestial interactions. Moreover, the multiscale nature of these processes presents a challenge to existing methods as a long time horizon is required. By leveraging a symplectic Lie-group manifold preserving integrator, we present a method for data-driven discovery of non-Newtonian astronomy. Preliminary results show the importance of both these properties in training stability and prediction accuracy.