A parametric model-order reduction method based on interpolation of reduced-order models, namely the pole-matching method, is proposed for linear systems in the frequency domain. It captures the parametric dynamics of the system by interpolating the positions and amplitudes of the poles. The pole-matching method relies completely on the reduced-order models themselves, regardless of how they are built. It is able to deal with many parameters as well as complicated parameter dependency. Numerical results show that the proposed pole-matching method gives accurate results even when it interpolates two reduced-order models of completely different nature, one computed by a projection-based method and the other computed by a data-driven method.
|ScienceOpen disciplines:||Applied mathematics, Applications, Statistics, Data analysis, Mathematics, Mathematical modeling & Computation|
|Keywords:||Modal Analysis, Parametric Model Order Reduction, Interpolation Method|