Guided Diffusion for Fast Inverse Design of Density-based Mechanical
  Metamaterials
Yanyan Yang Lili Wang Xiaoya Zhai Kai Chen Wenming Wu Yunkai Zhao Ligang Liu Xiao-Ming Fu
Mechanical metamaterial is a synthetic material that can possess
extraordinary physical characteristics, such as abnormal elasticity, stiffness,
and stability, by carefully designing its internal structure. To make
metamaterials contain delicate local structures with unique mechanical
properties, it is a potential method to represent them through high-resolution
voxels. However, it brings a substantial computational burden. To this end,
this paper proposes a fast inverse design method, whose core is an advanced
deep generative AI algorithm, to generate voxel-based mechanical metamaterials.
Specifically, we use the self-conditioned diffusion model, capable of
generating a microstructure with a resolution of $128^3$ to approach the
specified homogenized tensor matrix in just 3 seconds. Accordingly, this rapid
reverse design tool facilitates the exploration of extreme metamaterials, the
sequence interpolation in metamaterials, and the generation of diverse
microstructures for multi-scale design. This flexible and adaptive generative
tool is of great value in structural engineering or other mechanical systems
and can stimulate more subsequent research.