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Continuous Learning of Gravity Field Irregularities Around Small Bodies via Neural Hamiltonian ODEs

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

We propose to learn the unknown dynamics in the proximity of a small body directly from tracking data, representing them as a feed-forward neural network embedded in the system Hamiltonian. The equations of motion form a Neural Hamiltonian Ordinary Differential Equation, whose variational equations provide exact training gradients: estimation uses position and velocity arcs at realistic noise levels, without acceleration or potential labels, and a continual learning approach warm-starts the network as new data are acquired. The known part of the Hamiltonian carries whatever is available, from

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