Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.13570 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916280806670336 |
|---|---|
| author | Yang, Yanyan Wang, Lili Zhai, Xiaoya Chen, Kai Wu, Wenming Zhao, Yunkai Liu, Ligang Fu, Xiao-Ming |
| author_facet | Yang, Yanyan Wang, Lili Zhai, Xiaoya Chen, Kai Wu, Wenming Zhao, Yunkai Liu, Ligang Fu, Xiao-Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials Yang, Yanyan Wang, Lili Zhai, Xiaoya Chen, Kai Wu, Wenming Zhao, Yunkai Liu, Ligang Fu, Xiao-Ming Computational Engineering, Finance, and Science Machine Learning 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. |
| title | Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials |
| topic | Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2401.13570 |