Physics-informed Conditional Normalizing Flows for Angles-only Cislunar Orbit Determination
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Computer Science > Machine Learning
Title:Physics-informed Conditional Normalizing Flows for Angles-only Cislunar Orbit Determination
Abstract:Generative Astrodynamics is advanced in this work by extending generative modelling to an orbit determination problem in the cislunar environment. The task is formulated as conditional density estimation, aiming to infer the probability distribution of the initial state from angles-only measurements over short observation arcs. A normalising flow is trained on perturbed topocentric observations from Near Rectilinear Halo Orbits, enabling a flexible and potentially multimodal posterior representation. Given new measurements, the learned density is sampled to generate statistically consistent and physics-informed state hypotheses. These estimates are refined via nonlinear least-squares minimisation, providing a competitive warm start for classical algorithms.
| Subjects: | Machine Learning (cs.LG); Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.30936 [cs.LG] |
| (or arXiv:2606.30936v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.30936
arXiv-issued DOI via DataCite (pending registration)
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