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.

此论文介绍了一种快速反向设计方法，利用先进的深度生成人工智能算法生成基于体素的机械超材料，该方法能够在仅 3 秒内逼近所指定的均质张量矩阵的分辨率为 $128^3$ 的微结构。该工具对于结构工程或其他机械系统具有灵活和自适应的生成能力，可以激发更多的后续研究。