Nonlinear inverse design of mechanical multi-material metamaterials enabled by video denoising diffusion and structure identifier

Metamaterials, synthetic materials with customized properties, have emerged as a promising field due to advancements in additive manufacturing. These materials derive unique mechanical properties from their internal lattice structures, which are often composed of multiple materials that repeat geometric patterns. While traditional inverse design approaches have shown potential, they struggle to map nonlinear material behavior to multiple possible structural configurations. This paper presents a novel framework leveraging video diffusion models, a type of generative Artificial Intelligence (AI), for inverse multi-material design based on nonlinear stress–strain responses. Our approach consists of two key components: (1) a fields generator using a video diffusion model to create solution fields based on target nonlinear stress–strain responses, and (2) a structure identifier employing two U-shaped encoder–decoder network (UNet) architectures to determine the corresponding multi-material two-dimensional (2D) design. By incorporating multiple materials, plasticity, contact, and large deformation, our innovative design method allows for enhanced control over the highly nonlinear mechanical behavior of metamaterials commonly seen in real-world applications within 10% relative error from desired property. It offers a promising solution for generating next-generation metamaterials with finely tuned mechanical characteristics.