Machine learning models in phononic metamaterials

Abstract

Machine learning opens up a new avenue for advancing the development of phononic crystals and elastic metamaterials. Numerous learning models have been employed and developed to address various challenges in the field of phononic metamaterials. Here, we provide an overview of mainstream machine learning models applied to phononic metamaterials, discuss their capabilities as well as limitations, and explore potential directions for future research.