Deep learning for the design of phononic crystals and elastic metamaterials

Abstract

The computer revolution coming by way of data provides an innovative approach for the design of phononic crystals (PnCs) and elastic metamaterials (EMs). By establishing an analytical surrogate model for PnCs/EMs, deep learning based on artificial neural networks (ANNs) possesses the superiorities of rapidity and accuracy in design, making up for the shortcomings of traditional design methods. Here, the recent progresses on deep learning for forward prediction, parameter design, and topology design of PnCs and EMs are reviewed. The challenges and perspectives in this emerging field are also commented.