Port-Hamiltonian Discontinuous Galerkin Finite Element Methods
N. Kumar, J.J.W. van der Vegt, H.J. Zwart

TL;DR
This paper introduces a novel port-Hamiltonian discontinuous Galerkin finite element framework that preserves structure and energy flow, enabling accurate discretization of infinite-dimensional Hamiltonian systems with boundary interactions.
Contribution
It develops a new structure-preserving DG discretization for port-Hamiltonian systems, including a mathematical framework and error analysis, applicable to complex geometries.
Findings
Demonstrates optimal convergence rates for scalar wave equation
Provides a power-preserving coupling framework for DG discretizations
Establishes a priori error estimates for the proposed method
Abstract
A port-Hamiltonian (pH) system formulation is a geometrical notion used to formulate conservation laws for various physical systems. The distributed parameter port-Hamiltonian formulation models infinite dimensional Hamiltonian dynamical systems that have a non-zero energy flow through the boundaries. In this paper we propose a novel framework for discontinuous Galerkin (DG) discretizations of pH-systems. Linking DG methods with pH-systems gives rise to compatible structure preserving finite element discretizations along with flexibility in terms of geometry and function spaces of the variables involved. Moreover, the port-Hamiltonian formulation makes boundary ports explicit, which makes the choice of structure and power preserving numerical fluxes easier. We state the Discontinuous Finite Element Stokes-Dirac structure with a power preserving coupling between elements, which provides…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical methods for differential equations · Advanced Numerical Methods in Computational Mathematics · Model Reduction and Neural Networks
