17th International Symposium on
Mathematical Theory of Networks and Systems
Kyoto International Conference Hall, Kyoto, Japan, July 24-28, 2006

MTNS 2006 Paper Abstract

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Paper ThP03.2

Astrid, Patricia (Shell Global Solutions International B.V.), Verhoeven, Arie (Tech. Univ. Eindhoven)

Application of Least Squares MPE Technique in the Reduced Order Modeling of Electrical Circuits

Scheduled for presentation during the Mini-Symposium "Dimension reduction of large-scale systems II" (ThP03), Thursday, July 27, 2006, 15:45−16:10, Room C2

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on May 19, 2024

Keywords Model reduction, Large scale systems, Estimation of distributed parameter systems

Abstract

Reduced order models are usually derived by performing the Galerkin projection procedure, where the original equations are projected onto the space spanned by a set of approximating basis functions. For Differential Algebraic Equations this projection scheme may yield an unsolvable reduced order model. This means that a model of an electrical circuit can become ill-posed if it is reduced by the Galerkin technique. As a remedy to the problem, in this paper the reformulation of the reduced order model problem in the least squares sense is suggested. The space where the original is projected is different to the space used in the Galerkin procedure. It is shown that the resulting reduced order model will be guaranteed to be well-posed when the problem of finding a reduced order model is cast into a least squares problem.

To accelerate the reduced order modeling computation, the Missing Point Estimation (MPE) technique which was successfully implemented in the PDE-models of heat transfer processes is also applied to the least-square reduced order model of the electrical circuit. The least-square based MPE model is derived by projecting a subset of the original equation onto the least-square space. The dynamics of the stiff DAE model can be approximated very closely by a reduced order model built from less than 28% of the original equations.