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We propose a model order reduction approach for non-intrusive surrogatemodeling of parametric dynamical systems. The reduced model over the wholeparameter space is built by combining surrogates in frequency only, built atfew selected values of the parameters. This, in particular, requires matchingthe respective poles by solving an optimization problem. If the frequencysurrogates are constructed by a suitable rational interpolation strategy,frequency and parameters can both be sampled in an adaptive fashion. This, ingeneral, yields frequency surrogates with different numbers of poles, asituation addressed by our proposed algorithm. Moreover, we explain how ourmethod can be applied even in high-dimensional settings, by employinglocally-refined sparse grids in parameter space to weaken the curse ofdimensionality. Numerical examples are used to showcase the effectiveness ofthe method, and to highlight some of its limitations in dealing with unbalancedpole matching, as well as with a large number of parameters.

Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates