Abstract Machine learning (ML), as a component of artificial intelligence, encourages
structural design exploration which leads to new technological advancements. By …

Flexible manipulation of elastic and acoustic waves through phononic meta-structured
materials (PMSMs) has attracted a lot of attention in the last three decades and shows a …

Abstract Machine intelligence continues to rise in popularity as an aid to the design and
discovery of novel metamaterials. The properties of metamaterials are essentially …

Elastic metamaterial structures have many distinct wave properties such as band gap
structure and topological phase inversion. The coexistence and interaction of piezoelectricity …

Abstract Machine learning opens up a new avenue for advancing the development of
phononic crystals and elastic metamaterials. Numerous learning models have been …

We propose a novel approach for tackling scientific problems governed by differential
equations, based on the concept of a physics-informed neural networks (PINNs). The …

Current research on the inverse design of phononic crystals, which aim to retrieve optimal
structures according to given band gaps, is limited to controlling elastic waves at the macro …

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 …

Recent advances in machine learning have revolutionized numerous research domains by
extracting the hidden features and properties of complex systems, which are not otherwise …

Piezoelectric phononic crystals (PPCs) have the advantages of simplicity and efficiency in
controlling elastic wave bandgaps, which can be utilized to solve NVH problems with …