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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Coverage regularization in minimal MLPs yields lower prototype reconstruction error and higher specialization than baseline or repulsive losses on 1D data from N=3 to 100.
citing papers explorer
-
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.
-
From Latent Space to Training Data: Explainable Specialization in Minimal MLPs
Coverage regularization in minimal MLPs yields lower prototype reconstruction error and higher specialization than baseline or repulsive losses on 1D data from N=3 to 100.