A hybrid approach for solving the gravitational \(N\)-body problem with artificial neural networks

DOI10.1016/j.jcp.2023.112596arXiv2310.20398MaRDI QIDQ6198174

Veronica Saz Ulibarrena, Maxwell X. Cai, Elena Sellentin, Philipp Horn, Barry Koren, Simon Portegies Zwart

Publication date: 21 February 2024

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2310.20398

zbMATH Keywords

machine learninghybrid methodnumerical integratorplanetary systemsgravitational \(N\)-body problemphysics-aware neural networks

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical analysis (65-XX) Limit theorems in probability theory (60Fxx)

Cites Work

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• On Latin hypercube sampling
• Sympnets: intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Deep Hamiltonian networks based on symplectic integrators
• DeepXDE: A Deep Learning Library for Solving Differential Equations
• Geometric Numerical Integration

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