Dataset for the study of various error measures steering adaptive model order reduction algorithms in vibroacoustic applications

The dataset is associated with the university dissertation titled "Surrogate modeling of high-dimensional vibroacoustic problems using parametric model order reduction" by Harikrishnan K. Sreekumar.

To evaluate the accuracy of a reduced order model in the context of model order reduction in the frequency domain, a range of error measures are available. The error measures are extensively used in adaptive algorithms to build reduced order models of optimal dimensions with the least effort. The author performs a detailed study on some of the popular error measures using simple plate examples that are discussed in the thesis. This current dataset publication contains essential artifacts (computational notebook, model data and primary results) that enable reproducibility of the outcome presented in the dissertation.