Deep Neural Network-Based High-Precision Identification of Weak Stability Boundary Structures

TL;DR

This paper introduces a deep neural network approach for efficiently and accurately identifying weak stability boundary structures, significantly improving precision over traditional methods in space trajectory analysis.

Contribution

The paper presents a novel deep learning-based method for identifying weak stability boundary structures with high precision, addressing limitations of conventional techniques.

Findings

Achieved identification precision of 97.26-99.91%

Validated models effectively construct weak stability boundary structures

Provided insights into geometric and dynamical properties of the structures

Abstract

Weak stability boundary structures have been widely applied to the analysis on ballistic capture and the construction of low-energy transfers. The first step of this application is to compute/identify weak stability boundary structures. Conventional numerical and analytical methods cannot simultaneously achieve computational efficiency and identification precision. In this paper, we propose an efficient and precise method to identify weak stability boundary structures based on deep neural network. The geometric and dynamical properties of weak stability boundary structures are firstly analyzed, which provides further insights into the training of the deep neural network models. Then, the optimal hyperparameter combinations are determined by examining the identification precision of the trained deep neural network models. The performance of the models with the optimal hyperparameter combinations is further validated using the representative test datasets, achieving the precision of 97.26-99.91%. The trained models are also applied to constructing weak stability boundary structures.