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 $1.27\times {10}^4$, respectively.

受 Keplerian Map 和 Flyby Map 的启发，开发了一种使用高斯过程回归的重力辅助映射来解决全空间圆形受限三体问题。定义了用于量化一个轨道周期内飞越效应的映射函数。高斯过程回归模型是通过适当的均值和协方差函数建立的。该模型从训练样本中学习飞越的动力学，这些样本是通过数值传播生成的。为了提高该方法的效率，提出了一种新的标准来确定训练数据集的最佳大小。我们讨论了它的鲁棒性，以显示实际使用的质量。研究了不同输入元素对飞越效应的影响。已针对不同能量水平研究了所提出模型的准确性和效率，从具有代表性的高能量案例到低能量案例。它显示了对 Kick Map 的改进，Kick Map 是文献中一种独立的半分析方法。预测半长轴变化的准确性和效率提高了 3.3 倍，并且$1.27\times {10}^4$， 分别。