On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations
It addresses the problem of efficient simulation in vibroacoustics for researchers and engineers, but appears incremental as it applies existing ML methods to a specific domain.
This paper tackled the challenge of creating surrogate models for Sound Transmission Loss simulations in vibroacoustics by investigating four Machine Learning approaches, using feature importance and engineering to improve accuracy and interpretability, though no concrete numerical results are provided.
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.