Deep learning of dispersion engineering in two-dimensional phononic crystals
Xuan-Bo Miao, Hao‐Wen Dong, Yue‐Sheng Wang
Abstract
To control wave propagation in phononic crystals (PnCs), it is crucial to perform the inverse design of dispersion engineering. In this article, a robust deep-learning method of dispersion engineering in two-dimensional (2D) PnCs is developed by combining deep neural networks (DNNs) with the genetic algorithm (GA), which can be easily extended to reach any target in the trained DNNs’ calculation domain. A high-precision and robust DNN model to predict the bounds of energy bands of 2D PnCs is proposed, forming the forward prediction process. This DNN model shows high efficiency in the testing structures while keeping the mean relative error near 0.1%. The inverse design of PnCs is implemented by DNNs combined with the GA, building the back–forward retrieval process, which can exactly produce the desired PnCs with the expected bandgap bounds in only a few seconds. The proposed framework is promising for constructing arbitrary PnCs on demand.