Application Progress of machine learning in acoustic metamaterials

Acoustic metamaterials, as a new type of artificial composite structural materials possess numerous novel and anomalous physical properties which natural materials do not possess to provide new study avenues and application opportunities for effective control and precise regulation of sound waves. However, in order to obtain specific structural functional responses of acoustic metamaterials, traditional design methods require repeated adjustments of material Parameters or structural forms in processes of theoretical derivation, numerical Simulation and experimental verification to significantly increase study and computational costs. Machine learning has powerful nonlinear fitting capabilities, it can bypass physical modeling process with optimization algorithms and directly construct appropriate mapping relations in parametric space to reach the goal of meeting functional requirements, and provide the possibility to break through height limitations of traditional physical design strategies. Here, an overview of recent application progress of machine learning in acoustic metamaterials was summarized. Firstly, a brief overview of basic development of acoustic metamaterials and fundamental principles of mainstream machine learning algorithms was presented. Then, the latest application study results of machine learning in phononic crystals, acoustic metamaterials and acoustic metamaterial topology design were introduced in detail. Finally, the current study Status and prospects in this field were discussed and outlooked accordingly. © 2024 Chinese Vibration Engineering Society. All rights reserved.