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.

翻译：力学超材料是一种通过精心设计内部结构而具备异常弹性、刚度和稳定性等特殊物理特性的人造材料。为使超材料包含具有独特力学性能的精细局部结构，采用高分辨率体素进行表征是一种潜在方法，但这会带来巨大的计算负担。为此，本文提出一种快速逆向设计方法，其核心是一种先进的深度生成式人工智能算法，用于生成基于体素的力学超材料。具体而言，我们使用自条件扩散模型，能够在3秒内生成分辨率为$128^3$的微结构，以逼近指定的均匀化张量矩阵。因此，这种快速逆向设计工具有助于探索极端超材料、实现超材料的序列插值以及生成多样化的微结构以进行多尺度设计。这种灵活且自适应的生成工具在结构工程或其他力学系统中具有重要价值，并可激发更多后续研究。