The intelligent design in acoustic metamaterials: from optimization to inverse design

Acoustic metamaterials (AMs) possess unique acoustic properties and physical characteristics, positioning them as a key direction for future advancements in the field of acoustics. Traditional design methods, due to their time-consuming processes and resource inefficiency, no longer meet the practical demands of acoustic metamaterial design tasks. The emergence of artificial intelligence (AI) has sparked a surge in research on the intelligent design of AMs, establishing it as a novel approach to their design. This paper first introduces the AI technologies widely applied in the intelligent design of AMs, including machine learning (ML) and intelligent optimization algorithms (IOA), covering the classification and research progress of ML and IOA. It then focuses on exploring the potential applications of AI techniques in the intelligent design of AMs, with particular emphasis on the current state of research in structural optimization and inverse design. Finally, it analyzes the difficulties and bottlenecks faced by existing technologies in this field and provides a perspective on future developments.