DiffMat:利用扩散模型进行数据驱动的吸能超材料反设计

IF 6.9 1区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-17 DOI:10.1016/j.cma.2024.117440
Haoyu Wang , Zongliang Du , Fuyong Feng , Zhong Kang , Shan Tang , Xu Guo
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引用次数: 0

摘要

吸能材料和结构被广泛应用于工业领域。目前,吸能超材料的设计方法主要依靠经验或生物启发配置。受人工智能生成内容的启发,本文提出了一种新颖的吸能超材料反向设计框架,该框架采用名为 DiffMat 的扩散模型,可根据所需的应力应变曲线定制生成微结构。DiffMat 可根据机械特性学习微结构的条件分布,并实现从特性到几何形状的一对多映射。数值模拟和实验验证证明,DiffMat 能够根据给定的机械性能生成各种微观结构。这表明 DiffMat 在生成符合所需机械特性的超材料方面具有很高的有效性和准确性。所提出的反向设计框架的成功演示突出了其彻底改变吸能超材料开发的潜力,并强调了将人工智能启发方法整合到超材料设计和工程中的广泛影响。
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DiffMat: Data-driven inverse design of energy-absorbing metamaterials using diffusion model
Energy-absorbing materials and structures are widely applied in industrial areas. Presently, design methods of energy-absorbing metamaterials mainly rely on empirical or bio-inspired configurations. Inspired by AI-generated content, this paper proposes a novel inverse design framework for energy-absorbing metamaterial using diffusion model called DiffMat, which can be customized to generate microstructures given desired stress–strain curves. DiffMat learns the conditional distribution of microstructure given mechanical properties and can realize the one-to-many mapping from properties to geometries. Numerical simulations and experimental validations demonstrate the capability of DiffMat to generate a diverse array of microstructures based on given mechanical properties. This indicates the validity and high accuracy of DiffMat in generating metamaterials that meet the desired mechanical properties. The successful demonstration of the proposed inverse design framework highlights its potential to revolutionize the development of energy-absorbing metamaterials and underscores the broader impact of integrating AI-inspired methodologies into metamaterial design and engineering.
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来源期刊
CiteScore
12.70
自引率
15.30%
发文量
719
审稿时长
44 days
期刊介绍: Computer Methods in Applied Mechanics and Engineering stands as a cornerstone in the realm of computational science and engineering. With a history spanning over five decades, the journal has been a key platform for disseminating papers on advanced mathematical modeling and numerical solutions. Interdisciplinary in nature, these contributions encompass mechanics, mathematics, computer science, and various scientific disciplines. The journal welcomes a broad range of computational methods addressing the simulation, analysis, and design of complex physical problems, making it a vital resource for researchers in the field.
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