Interpretable and Physics-Supported Machine Learning Model for Sound Transmission Loss Analysis

Autor: Cunha, Barbara, Ichchou, Mohamed, Droz, Christophe, Zine, Abdelmalek, Foulard, Stéphane
Přispěvatelé: Cunha, Barbara, Lightening and Innovating transmission for improving Vehicle Environmental Impacts - LIVE-I - INCOMING, Laboratoire de Tribologie et Dynamique des Systèmes (LTDS), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Ecole Nationale d'Ingénieurs de Saint Etienne (ENISE)-Centre National de la Recherche Scientifique (CNRS), Structure et Instrumentation Intégrée (COSYS-SII ), Université Gustave Eiffel, Statistical Inference for Structural Health Monitoring (I4S), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département Composants et Systèmes (COSYS), Université Gustave Eiffel-Université Gustave Eiffel, Institut Camille Jordan (ICJ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Modélisation mathématique, calcul scientifique (MMCS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Compredict GmbH [Darmstadt], This work received financial support of the European Union’s Horizon 2020 research and innovation programunder Marie-Curie grant agreement No 860243 to the LIVE-I project., European Project: LIVE-I
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: ISMA 2022-International Conference on Noise and Vibration Engineering
ISMA 2022-International Conference on Noise and Vibration Engineering, Sep 2022, Leuven, Belgium. pp.1-15
Popis: International audience; Lately, there has been a growing interest in applying Machine Learning and Digital Twins for the speed-up of acoustic simulations. However, the lack of interpretability and physics foundation inhibit the widespread usage of these black-box models by the scientific community. In this article, global sensitivity analysis and feature engineering techniques are leveraged to improve the interpretability and physical consistency of MLbased simulations of the Sound Transmission Loss problem for a variety of plate materials. Computationally efficient sensitivity analysis is obtained via the Mean Decrease in Impurity, which is the byproduct of the training of the Random Forest surrogate models. The resulting sensitivity indices were shown to be similar to the traditional Sobol indices and more accurate than Fourier amplitude sensitivity testing for small datasets. Moreover, introducing basic expert knowledge into the ML inputs helped reduce the surrogate prediction error and interpret the physical meaning of sensitivity indices throughout the frequency spectrum.
Databáze: OpenAIRE
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