Abstract
Shape-morphing metamaterials enable adaptive structures capable of complex functional deformations, with applications ranging from reconfigurable structures and soft robotics to medical devices. However, their design remains challenging due to an inherent trade-off between deformation programmability and computational scalability. Periodic architectures offer computational tractability but are limited in their programmability, whereas aperiodic metamaterials provide richer deformation spaces at the cost of substantially increased design complexity. To bridge this gap, we propose a scalable active metamaterial (SAM) design framework that decouples the design problem into two scales by exploiting the local deformation independence of units isolated by stiff structural members. At the macroscale, global shape deformation is determined by iteratively solving a constrained mesh optimization problem incorporating data-driven constraints. At the microscale, the local infill geometry is obtained through inverse design via either a conditional diffusion model or an adjustable search strategy. This hierarchical decomposition enables fast, accurate, and scalable design of aperiodic shape-morphing metamaterials, offering a new computational paradigm for the design of programmable material systems.
Wei (Wayne) Chen
Assistant Professor of Mechanical Engineering
My research interests include AI, machine learning, and their applications to engineering design.