Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials

• URL: http://arxiv.org/abs/2401.13570v2
• Date: Mon, 10 Jun 2024 11:26:14 GMT
• Title: Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials
• Authors: Yanyan Yang, Lili Wang, Xiaoya Zhai, Kai Chen, Wenming Wu, Yunkai Zhao, Ligang Liu, Xiao-Ming Fu,
• Abstract summary: 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 $1283$ to approach the specified homogenized matrix in just 3 seconds.
• Score: 41.97258566607252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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