Efficient n-body simulations using physics informed graph neural networks

• URL: http://arxiv.org/abs/2504.01169v1
• Date: Tue, 01 Apr 2025 20:23:34 GMT
• Title: Efficient n-body simulations using physics informed graph neural networks
• Authors: Víctor Ramos-Osuna, Alberto Díaz-Álvarez, Raúl Lara-Cabrera,
• Abstract summary: This paper implements a leapfrog-based simulation engine to generate datasets from diverse astrophysical scenarios.<n>A custom-designed GNN is trained to predict particle accelerations with high precision.<n>Our method yields a modest speedup of approximately 17% over conventional simulation techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents a novel approach for accelerating n-body simulations by integrating a physics-informed graph neural networks (GNN) with traditional numerical methods. Our method implements a leapfrog-based simulation engine to generate datasets from diverse astrophysical scenarios which are then transformed into graph representations. A custom-designed GNN is trained to predict particle accelerations with high precision. Experiments, conducted on 60 training and 6 testing simulations spanning from 3 to 500 bodies over 1000 time steps, demonstrate that the proposed model achieves extremely low prediction errors-loss values while maintaining robust long-term stability, with accumulated errors in position, velocity, and acceleration remaining insignificant. Furthermore, our method yields a modest speedup of approximately 17% over conventional simulation techniques. These results indicate that the integration of deep learning with traditional physical simulation methods offers a promising pathway to significantly enhance computational efficiency without compromising accuracy.

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