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.
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Neural surrogates trained on a large homotopy-ray dataset approximate low-thrust fuel consumption and transfer times, obey a scaling law, and generalize via self-similar transformation across semi-major axes, inclinations, and central bodies.
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Tiny Recursive Models for Solving the J2-Perturbed Lambert Problem
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.
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Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability
Neural surrogates trained on a large homotopy-ray dataset approximate low-thrust fuel consumption and transfer times, obey a scaling law, and generalize via self-similar transformation across semi-major axes, inclinations, and central bodies.