A gravity assist mapping for the circular restricted three-body problem using Gaussian processes

Yuxin Liu*, Ron Noomen, Pieter Visser

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
64 Downloads (Pure)


Inspired by the Keplerian Map and the Flyby Map, a Gravity Assist Mapping using Gaussian Process Regression for the fully spatial Circular Restricted Three-Body Problem is developed. A mapping function for quantifying the flyby effects over one orbital period is defined. The Gaussian Process Regression model is established by proper mean and covariance functions. The model learns the dynamics of flyby's from training samples, which are generated by numerical propagation. To improve the efficiency of this method, a new criterion is proposed to determine the optimal size of the training dataset. We discuss its robustness to show the quality of practical usage. The influence of different input elements on the flyby effects is studied. The accuracy and efficiency of the proposed model have been investigated for different energy levels, ranging from representative high- to low-energy cases. It shows improvements over the Kick Map, an independent semi-analytical method available in literature. The accuracy and efficiency of predicting the variation of the semi-major axis are improved by factors of 3.3, and 1.27×104, respectively.

Original languageEnglish
Pages (from-to)2488-2500
Number of pages13
JournalAdvances in Space Research
Issue number6
Publication statusPublished - 2021


  • Gaussian process regression
  • Gravity assist mapping
  • Machine learning

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