On Machine-Learning-Driven Surrogates for Sound Transmission Loss Simulations

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. Experiments show that neural network surrogates with physics-guided features have better accuracy than other ML models across different STL models. Furthermore, sensitivity analysis methods are used to assess how physically coherent the analyzed surrogates are.

Automated summary

This paper investigates the modelling of surrogate models for sound transmission loss (STL) in the vibroacoustic domain using four machine learning approaches. Feature engineering and feature importance are utilized to improve accuracy and interpretability. Results show that neural network surrogates with physics-guided features outperform other ML models in different STL models.