On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Applied Sciences Année : 2022

On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations

Résumé

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.
Fichier principal
Vignette du fichier
text.pdf (1.82 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03639282 , version 1 (22-04-2022)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Barbara Zaparoli Cunha, Abdelmalek Zine, Mohamed Ichchou, Christophe Droz, Stéphane Foulard. On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations. Applied Sciences, 2022, ⟨10.3390/app122110727⟩. ⟨hal-03639282⟩
103 Consultations
96 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More