Generative Design of Periodic Orbits in the Restricted Three-Body Problem

TL;DR

This paper explores using Variational Autoencoders to generate and analyze periodic orbits in the Circular Restricted Three-Body Problem, aiming to improve space mission design and astrodynamics understanding.

Contribution

It introduces a deep learning approach employing VAEs to generate and evaluate periodic orbits in the CR3BP, advancing data-driven methods in astrodynamics.

Findings

VAE effectively captures key orbital features

Generated orbits meet physical evaluation metrics

Method shows potential for space mission planning

Abstract

The Three-Body Problem has fascinated scientists for centuries and it has been crucial in the design of modern space missions. Recent developments in Generative Artificial Intelligence hold transformative promise for addressing this longstanding problem. This work investigates the use of Variational Autoencoder (VAE) and its internal representation to generate periodic orbits. We utilize a comprehensive dataset of periodic orbits in the Circular Restricted Three-Body Problem (CR3BP) to train deep-learning architectures that capture key orbital characteristics, and we set up physical evaluation metrics for the generated trajectories. Through this investigation, we seek to enhance the understanding of how Generative AI can improve space mission planning and astrodynamics research, leading to novel, data-driven approaches in the field.