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arXiv · 2408.03691

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

Alvaro Francisco Gil·Walther Litteri·Victor Rodriguez-Fernandez·David Camacho·Massimiliano Vasile
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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.

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Alvaro Francisco Gil, Walther Litteri, Victor Rodriguez-Fernandez, David Camacho, Massimiliano Vasile. 2024-08-07. Generative Design of Periodic Orbits in the Restricted Three-Body Problem. https://doi.org/10.5281/zenodo.13885649

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