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In modeling and simulation of large-scale systems, Model Order Reduction (MOR), and specifically parametric MOR (pMOR), has grown in importance recently. In this paper, a concept based on distance between subspaces has been automated and combined with the efficient parametric reduction approach Matrix Interpolation to give an automatic adaptive sampling strategy in pMOR. An algorithm is developed...
We present rigorous bounds on the ℌ2 and ℌ∞ norm of the error resulting from model order reduction of second order systems by KRYLOV subspace methods. To this end, we use a strictly dissipative state space realization of the model and perform a factorization of the error system. The derived error expressions are easy to compute and can therefore be applied to models of very high order, as is demonstrated...
A general framework for model order reduction is proposed for high-order parameter-dependent, linear time-invariant systems. The procedure is based on matrix interpolation and consists of six steps. At first a set of high-order nonparametric systems is computed for different parameter vectors. The resulting local high-order systems are then reduced by a projection-based reduction method. Thereby,...
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