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arXiv · 2606.25893

A Physics-Informed Statistical Learning Model for Long-Term Fragmentation Cloud Propagation

Yema Paul·Diogo Sousa·Albert Servitje·Rodrigo Pariente·Emmanuel Delande·Christophe Taillan·Joan Pau Sanchez
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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.

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Yema Paul, Diogo Sousa, Albert Servitje, Rodrigo Pariente, Emmanuel Delande, Christophe Taillan, Joan Pau Sanchez. 2026-06-24. A Physics-Informed Statistical Learning Model for Long-Term Fragmentation Cloud Propagation. https://arxiv.org/abs/2606.25893

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