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Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian
Oswin So · Gongjie Li · Evangelos Theodorou · Molei Tao

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in Machine Learning and the Physical Sciences »

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.

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Author Information

Oswin So (Massachusetts Institute of Technology)
Gongjie Li (Georgia Institute of Technology)
Evangelos Theodorou (Georgia Institute of Technology)
Molei Tao (Georgia Institute of Technology)

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