Machine learning assisted intelligent design of meta structures: a review
Liangshu He, Yan Li, Daniel Torrent, Xiaoying Zhuang, Timon Rabczuk, Yabin Jin
Abstract
In recent years, the rapid development of machine learning (ML) based on data-driven or environment interaction has injected new vitality into the field of meta-structure design. As a supplement to the traditional analysis methods based on physical formulas and rules, the involvement of ML has greatly accelerated the pace of performance exploration and optimization for meta-structures. In this review, we focus on the latest progress of ML in acoustic, elastic, and mechanical meta-structures from the aspects of band structures, wave propagation characteristics, and static characteristics. We finally summarize and envisage some potential research directions of ML in the field of meta-structures.