A computationally efficient strategy is presented for adjusting analytic rotordynamic models to make them consistent with experimental data. The approach permits use of conventional rotordynamic models derived using finite element methods in conjunction with conventional plant identification models derived from impact or sine sweep testing in a transfer function or influence coefficient format. The underlying assumption is that the predominant uncertainties in engineered models occur at discrete points as effects like shrink fits, seal coefficients or foundation interactions. Further, it is assumed that these unmodeled or poorly modeled effects are essentially linear (at least within the testing and expected operating domains). Matching is accomplished by deriving a dynamic model for these uncertain effects such that the resulting composite model has a transfer function which matches that obtained experimentally. The derived augmentations are computationally compatible with the original rotor model and valid for stability or forced response predictions. Further, computation of this augmentation is accomplished using well developed and widely disseminated tools for modern control. Background theory and a complete recipe for the solution are supported by a number of examples.