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arxiv: 1706.08621 · v1 · pith:SAMH4UGDnew · submitted 2017-06-26 · 🧮 math.NA

Energy-Preserving and Passivity-Consistent Numerical Discretization of Port-Hamiltonian Systems

classification 🧮 math.NA
keywords discretemethodsport-hamiltoniansystemsgradientnumericalapplyingapproach
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In this paper we design discrete port-Hamiltonian systems systematically in two different ways, by applying discrete gradient methods and splitting methods respectively. The discrete port-Hamiltonian systems we get satisfy a discrete notion of passivity, which lets us, by choosing the input appropriately, make them globally asymptotically stable with respect to an equilibrium point. We test methods designed using the discrete gradient approach in numerical experiments, and the results are encouraging when compared to relevant existing integrators of identical order.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Identifying the nonlinear string dynamics with port-Hamiltonian neural networks

    cs.LG 2026-05 unverdicted novelty 7.0

    Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.

  2. Structure-preserving space discretization of differential and nonlocal constitutive relations for port-Hamiltonian systems

    math.NA 2025-07 unverdicted novelty 6.0

    Structure-preserving finite element discretization of port-Hamiltonian systems with differential constitutive relations via Stokes-Lagrange structures, applied to nanorod, shear beam, and nonlinear 2D Navier-Stokes mo...