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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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.
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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.
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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.