Efficient Error Estimator for Model Order Reduction of Linear Parametric Systems

We propose an error estimator for reduced-order modeling of linear parametric dynamical systems. The error estimator can be easily extended to output error estimation of reduced-order models for steady linear parametric systems. It is sharp and cheap to compute. Using the error estimator, the reduced-order model can be adaptively obtained with high reliability. Numerical results show that the error estimator can accurately estimate the true error even for transfer functions with many resonances. Compared with an existing error bound, the proposed error estimator can be orders of magnitudes sharper and needs much less computational time.