In this paper, a new framework for model order reduction of LTI parametric systems is introduced. After generating and reducing several local original models in the parameter space, a parametric reduced-order model is calculated by interpolating the system matrices of the local reduced models. The main task is to find compatible system representations with optimal interpolation properties. Two approaches for this purpose are presented together with several numerical simulations.Zusammenfassung In diesem Beitrag wird ein neuer Rahmen zur Modellordnungsreduktion parametrischer LZI-Systeme vorgestellt. Er sieht zunächst die Reduktion des Originalmodells in einigen Stützstellen des Parameterraums vor. Anschließend wird ein reduziertes parametrisches System generiert, indem die Systemmatrizen der lokalen Reduktionen durch geeignete Transformationen kompatibel gemacht und interpoliert werden. Hierfür werden zwei alternative Verfahren beschrieben, deren Eigenschaften an drei numerischen Testfällen dargestellt werden.

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In this article, a method to preserve stability in parametric model reduction by matrix interpolation is presented. Based on the matrix measure approach, sufficient conditions on the original system matrices are derived. Once they are fulfilled, the stability of each of the reduced models is guaranteed as well as that of the parametric model resulting from interpolation. In addition, it is shown that these sufficient conditions are met by port-Hamiltonian systems and by a relevant set of second-order systems obtained by the finite element method. The new approach is illustrated by two numerical examples.

Two approaches for approximating the solution of large-scale Lyapunov equations are considered: the alternating direction implicit (ADI) iteration and projective methods by Krylov subspaces. A link between them is presented by showing that the ADI iteration can always be identified by a Petrov-Galerkin projection with rational block Krylov subspaces. Then a unique Krylov-projected dynamical system can be associated with the ADI iteration, which is proven to be an H 2 pseudo-optimal approximation. This includes the generalization of previous results on H 2 pseudo-optimality to the multivariable case. Additionally, a low-rank formulation of the residual in the Lyapunov equation is presented, which is well-suited for implementation, and which yields a measure of the "obliqueness" that the ADI iteration is associated with.