A generalized framework of neural networks for Hamiltonian systems

DOI10.1016/J.JCP.2024.113536MaRDI QIDQ6670713

Philipp Horn, Veronica Saz Ulibarrena, Simon Portegies Zwart, B. Koren

Publication date: 24 January 2025

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

zbMATH Keywords

neural networksHamiltonian systemsstructure preservationsymplectic algorithmsscientific machine learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computational methods for problems pertaining to astronomy and astrophysics (85-08) Numerical methods for ordinary differential equations (65Lxx)

Cites Work

• 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
• A proposal on machine learning via dynamical systems
• Stable architectures for deep neural networks
• Polynomial approximations of symplectic dynamics and richness of chaos in non-hyperbolic area-preserving maps
• Geometric Numerical Integration
• Beitrag zur näherungsweisen Integration totaler Differentialgleichungen.

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