  Surrogate models are data-based approximations of computationally expensive
simulations that enable efficient exploration of the model's design space and
informed decision-making in many physical domains. The usage of surrogate
models in the vibroacoustic domain, however, is challenging due to the
non-smooth, complex behavior of wave phenomena. This paper investigates four
Machine Learning (ML) approaches in the modelling of surrogates of Sound
Transmission Loss (STL). Feature importance and feature engineering are used to
improve the models' accuracy while increasing their interpretability and
physical consistency. The transfer of the proposed techniques to other problems
in the vibroacoustic domain and possible limitations of the models are
discussed.
