Variational learning of Euler-Lagrange dynamics from data

DOI10.1016/j.cam.2022.114780zbMath1498.65222arXiv2112.12619OpenAlexW4294946167MaRDI QIDQ2095136

Christian Offen, Sina Ober-Blöbaum

Publication date: 9 November 2022

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.12619

zbMATH Keywords

variational integratorsmodified LagrangianLagrangian learningphysics informed learningvariational backward error analysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) System identification (93B30) Numerical methods for Hamiltonian systems including symplectic integrators (65P10)

Related Items (3)

Backward error analysis for conjugate symplectic methods ⋮ Learning Hamiltonian systems with mono-implicit Runge-Kutta methods ⋮ Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves

Uses Software

• GitHub
• SympGPR

Cites Work

• Modified equations for variational integrators
• Sympnets: intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
• Backward error analysis for variational discretisations of PDEs
• Discrete mechanics and variational integrators
• Numerical integrators based on modified differential equations
• Symplectic Gaussian process regression of maps in Hamiltonian systems

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