Learning Hamiltonian systems with mono-implicit Runge-Kutta methods

DOI10.1007/978-3-031-38271-0_55arXiv2303.03769OpenAlexW4385435014MaRDI QIDQ6178889

Håkon Noren

Publication date: 16 January 2024

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2303.03769

zbMATH Keywords

inverse problemsHamiltonian systemsdeep neural networksmono-implicit Runge-Kutta

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Numerical solution of inverse problems involving ordinary differential equations (65L09)

Related Items (1)

Pseudo-Hamiltonian neural networks for learning partial differential equations

Cites Work

• A family of embedded Runge-Kutta formulae
• Optimal discrete and continuous mono-implicit Runge-Kutta schemes for BVODEs
• Deep neural networks motivated by partial differential equations
• Variational learning of Euler-Lagrange dynamics from data
• A proposal on machine learning via dynamical systems
• A Class of Implicit Runge-Kutta Methods for the Numerical Integration of Stiff Ordinary Differential Equations
• Numerical Optimization
• Order Results for Mono-Implicit Runge–Kutta Methods
• Stable architectures for deep neural networks
• Deep Hamiltonian networks based on symplectic integrators
• Geometric Numerical Integration
• Numerical Methods for Ordinary Differential Equations
• Symplectic learning for Hamiltonian neural networks

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