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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The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.
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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.
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Machine Learning as a Transformative Tool for (Exo-)Planetary Science
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.