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

doi:10.1007/s12204-023-2655-2

Abstract

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.

Key words: membrane-type acoustic metamaterial, sound transmission loss, eigenfrequency, physics-guided neural network, architecture search, Gini impurity, gray wolf optimizer, initial methods

摘要： 随着机器学习的快速发展，采用神经网络进行声学超材料的性能预测正在取代以实验为基础的传统测试方法。本文提出了基于基尼不纯度的神经网络结构优化器，并研究了五种初始化算法对于模型性能及结构优化的影响。为了进一步提升模型的预测精度，实现了混合共振频率和传声损失公式的两类物理引导模型。结果表明采用灰狼算法作为初始化方法的结构优化器能够显著提升模型的预测精度。同时，混合共振频率的物理引导模型拥有最佳性能且能更好地预测边缘数据点。最后，结合敏感度分析和理论公式解释了输入参数对于传声损失的影响。

关键词: 薄膜型声学超材料，传声损失，固有频率，物理引导神经网络，结构搜索，基尼不纯度，灰狼优化，初始化方法

CLC Number:

TB53
TP18

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]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 613-624.

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