SAP-Net: Deep learning to predict sound absorption performance of metaporous materials

Abstract

Airborne sound absorption coefficient is the premise for investigating the sound absorption performance or mechanism of metaporous materials. The common numerical evaluation approach is FEM which is relatively computationally costly particularly when processing complex structures or a large batch of data. Rapidly developing deep learning algorithms, on the other hand, show a promising trend in the data-driven manner to learn and predict material parameters efficiently and precisely. We propose SAP-net based on deep convolutional neural network to predict the sound absorption coefficient at a specific frequency of an input image representing the topological structure of metaporous materials. Trained with FEM-prepared data for six frequency points, SAP-net demonstrates outstanding evaluation speed of 0.007 s/image and brilliant prediction accuracy with mean absolute errors all smaller than 0.019 (the smallest 0.008 at f = 1000 Hz). Meanwhile, the fact that SAP-net remains accurate when predicting for images that are essentially different from those in the training data shows its capability of learning and capturing the underlying physical mechanism linking the topological structure to the sound absorption performance. In conclusion, SAP-net provides an extraordinarily fast and accurate approach for the investigation of sound absorption performance, which is expected to accelerate the examination and design process of materials.

Keywords

• Sound Absorption Coefficient Prediction
• Convolutional Neural Networks
• Metaporous Materials