Expressiveness and structure preservation in learning port-Hamiltonian systems

DOI10.1007/978-3-031-38299-4_33arXiv2303.02699OpenAlexW4385417340MaRDI QIDQ6179016

Daiying Yin, Juan-Pablo Ortega

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/2303.02699

zbMATH Keywords

machine learningstructure-preserving algorithmsystems theorylinear port-Hamiltonian systemphysics-informed machine learning

Mathematics Subject Classification ID

Controllability (93B05) Linear systems in control theory (93C05) Canonical structure (93B10) Observability (93B07)

Cites Work

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• Inferring solutions of differential equations using noisy multi-fidelity data
• Dimension reduction in recurrent networks by canonicalization
• Simulating Hamiltonian Dynamics
• Discrete mechanics and variational integrators
• Port-Hamiltonian Systems Theory: An Introductory Overview
• Geometric integrators for ODEs
• Mathematical Description of Linear Dynamical Systems

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