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research article

Structure-Preserving Reduced Basis Methods For Poisson Systems

Hesthaven, Jan S.  
•
Pagliantini, Cecilia  
July 1, 2021
Mathematics Of Computation

We develop structure-preserving reduced basis methods for a large class of nondissipative problems by resorting to their formulation as Hamiltonian dynamical systems. With this perspective, the phase space is naturally endowed with a Poisson manifold structure which encodes the physical properties, symmetries, and conservation laws of the dynamics. The goal is to design reduced basis methods for the general state-dependent degenerate Poisson structure based on a two-step approach. First, via a local approximation of the Poisson tensor, we split the Hamiltonian dynamics into an "almost symplectic" part and the trivial evolution of the Casimir invariants. Second, canonically symplectic reduced basis techniques are applied to the nontrivial component of the dynamics, preserving the local Poisson tensor kernel exactly. The global Poisson structure and the conservation properties of the phase flow are retained by the reduced model in the constant-valued case and up to errors in the Poisson tensor approximation in the state-dependent case. A priori error estimates for the solution of the reduced system are established. A set of numerical simulations is presented to corroborate the theoretical findings.

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Type
research article
DOI
10.1090/mcom/3618
Web of Science ID

WOS:000657115000007

Author(s)
Hesthaven, Jan S.  
Pagliantini, Cecilia  
Date Issued

2021-07-01

Publisher

AMER MATHEMATICAL SOC

Published in
Mathematics Of Computation
Volume

90

Issue

330

Start page

1701

End page

1740

Subjects

Mathematics, Applied

•

Mathematics

•

hamiltonian dynamics

•

poisson manifolds

•

symplectic structure

•

invariants of motion

•

structure-preserving schemes

•

reduced basis methods (rbm)

•

nonlinear model-reduction

•

discretization

•

convergence

•

schemes

•

theorem

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
MCSS  
Available on Infoscience
July 3, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/179640
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