In this paper, a new structure-preserving scheme for the reduction of linear port-Hamiltonian systems with dissipation using Krylov subspaces is presented. It is shown how to choose the projection matrices in order to guarantee the moment matching property and to obtain a passive and thus stable reduced-order model in port-Hamiltonian form. The method is suitable for the reduction of largescale systems as it employs only the well-known Arnoldi algorithm and matrix-vector multiplications to compute the reduced-order model. Afinite element model is reduced to illustrate the new method.
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In this paper, a new structure-preserving scheme for the reduction of linear port-Hamiltonian systems with dissipation using Krylov subspaces is presented. It is shown how to choose the projection matrices in order to guarantee the moment matching property and to obtain a passive and thus stable reduced-order model in port-Hamiltonian form. The method is suitable for the reduction of largescale systems as it employs only the well-known Arnoldi algorithm and matrix-vector multiplications to comp...
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