On demand design of two-dimensional phononic crystal bandgap based on machine learning and multi-objective topology optimization
Nansha Gao, Jiacheng Guo, Mou Wang, Yilin Qu, X Peng, Ye Tang, Xiao Liang, Guang Pan
Abstract
To address the issue of human resource wastage and the lack of direction in phononic crystal design, this study adopts the network structures of variational autoencoder, multi-layer perceptron, and twin neural network, to achieve high-precision forward and inverse predictions for two-dimensional phononic crystals. Five-fold cross-validation was conducted on the dataset, and the resulting accuracy demonstrated the strong generalization capability and robustness of the model structures of the multi-layer perceptron and twin neural network. Specifically, 90% of the accuracy values in forward predictions are equal to or greater than 0.98, while 98% of the accuracy values in inverse predictions are no less than 0.95. From a practical design standpoint, by incorporating a loss function into the twin neural network while considering both lightweight and bandgap performance, we can achieve high-precision, on-demand design with multiple objectives. The approach and methodology presented in this study offer significant insights for the rapid and accurate development of composite or structured materials.