Introduces maximum-initial-mass optimal control problem for low-thrust transfers, establishes correspondence to minimum-time extremals, and applies it to recover global solutions for a GTO-to-GEO benchmark.
Com- puting Low-Thrust Transfers in the Asteroid Belt, a Comparison between Astrodynamical Manipula- tions and a Machine Learning Approach
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces a maximum-initial-mass dual formulation that turns reachability into scalar optimization per target, approximated by residual neural networks for fast evaluation.
Neural surrogates trained with scaling laws and self-similar transformations accurately approximate low-thrust trajectory costs and reachability while generalizing across orbital parameters.
citing papers explorer
-
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.