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Journal of Shanghai Jiaotong University(Science) >

2025 , Vol. 30 >Issue 3: 613 - 624

DOI: https://doi.org/10.1007/s12204-023-2655-2

Medicine-Engineering Interdisciplinary

Physics-Guided Neural Network with Gini Impurity-Based Structural Optimizer for Prediction of Membrane-Type Acoustic Material Transmission Loss

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  • 1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; 2. School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2022-10-28

  Accepted date: 2023-01-05

  Online published: 2025-06-06

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Abstract

With the rapid development of machine learning, the prediction of the performance of acoustic metamaterials using neural networks is replacing the traditional experiment-based testing methods. In this paper, a Gini impurity-based artificial neural network structural optimizer (GIASO) is proposed to optimize the neural network structure, and the effects of five different initialization algorithms on the model performance and structure optimization are investigated. Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model. The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model. Simultaneously, the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points. Eventually, the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.

Cite this article

Pan Xinrong, Liu Xuewen, Zhu Bo, Wang Yingyi . Physics-Guided Neural Network with Gini Impurity-Based Structural Optimizer for Prediction of Membrane-Type Acoustic Material Transmission Loss[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(3) : 613 -624 . DOI: 10.1007/s12204-023-2655-2

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