TRM-PL uses a 2.3M-parameter weight-shared recursive architecture to reduce median position errors to 0.027 km on single-revolution LEO and 0.31 km on multi-revolution LEO transfers via position-supervised refinement.
Izzo, Revisiting Lambert’s problem, Celestial Mechanics and Dynamical Astronomy 121 (1) (2015) 1–15
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Neural surrogates trained with scaling laws and self-similar transformations accurately approximate low-thrust trajectory costs and reachability while generalizing across orbital parameters.
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Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability
Neural surrogates trained with scaling laws and self-similar transformations accurately approximate low-thrust trajectory costs and reachability while generalizing across orbital parameters.