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A Physics-Informed Statistical Learning Model for Long-Te...
[Submitted on 24 Jun 2026] · 2026-06-25 · via math updates on arXiv.org

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Abstract:This paper introduced a Hierarchical Generative Density Model (HGDM) for the long-term propagation of orbital fragmentation clouds. Validation against high-fidelity Monte Carlo simulations showed that the proposed surrogate accurately reproduces the dominant multidimensional structures of propagated clouds while consistently outperforming classical band-formation approximations based on independent angular variables. Accurate cloud reconstructions were obtained using only a few hundred to a few thousand propagated fragments, yielding reductions exceeding two orders of magnitude in computational cost and three orders of magnitude in storage requirements; future work will investigate its application to large-scale debris-environment evolution and collision-cascade simulations associated with the Kessler syndrome.

Submission history

From: Yema Paul [view email]
[v1] Wed, 24 Jun 2026 14:40:41 UTC (16,069 KB)