Intelligent on-demand design of phononic metamaterials

Abstract

With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficiency of the designs may be not sufficient. With the help of third-wave artificial intelligence technologies, the design schemes of these materials are undergoing a new revolution. As an important branch of artificial intelligence, machine learning paves the way to new technological innovations by stimulating the exploration of structural design. Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures. Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design. In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. Then discuss machine learning driven on-demand design of phononic metamaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties. Finally, we summarize the current state of the art and provide a prospective of the future development directions.

Optimized structures for vibration attenuation and sound control in nature: A review

“…258 Many of these approaches are being applied more and more in the field of phononics. 259 The types of optimized structures emerging from these algorithms have some common traits with naturally evolved structures and some distinctive differences. On the one hand, recurring features are many of those cited in the section ''Conclusions on impact-resistant structures'': heterogeneity, porosity, hierarchical organization, efficient resonating structure, graded properties, and in some cases chirality.…”

Exceptional Points and Skin Modes in Non-Hermitian Metabeams

“…In this manner, the proposed metabeam has the merit of versatile operation and efficient tunability. Our results present a design of a non-Hermitian 1D elastic wave system, which may further enrich manipulation techniques for elastic waves based on phononic crystals and metamaterials [38,39].…”

Tailoring Structure‐Borne Sound through Bandgap Engineering in Phononic Crystals and Metamaterials: A Comprehensive Review

“…This has led to inverse‐design based studies that start with the desired BG configuration or functionality and employ optimization approaches to arrive at the geometry and material that is required to achieve them. Most of these efforts largely rely on topology optimization, [ 56,57,411–436 ] genetic algorithms, [ 215,432,437,438 ] or machine learning‐based approaches [ 439–446 ] and have unveiled unusual and hence previously inconceivable geometries that enable materials with enhanced BG characteristics. While these efforts are now burgeoning thanks to modern computational power, one of the earliest fruitful strides in this direction for elastic waves, can be attributed to the works of Sigmund and Søndergaard Jensen [ 411 ] in the early 2000s, who first put forward a theoretical framework showing that phononic BGs could be considerably enlarged by opening the design space of the unit cell geometry, while enforcing the boundary conditions as constraints—in other words, via topology optimization.…”

“…For additional details on each of the optimization techniques discussed here, the reader is referred to the previously published review papers. [413,441,449]…”