用于结构化超材料统一生成设计的引导拓扑分布
Steering topology distributions for unified generative design of architected metamaterials
- Imperial College London（伦敦帝国理工学院）
- Tsinghua University（清华大学）
- Zhejiang University（浙江大学）
- Harbin Institute of Technology（哈尔滨工业大学）
- Southwest Jiaotong University（西南交通大学）
- Ningbo University（宁波大学）
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究结构化超材料设计问题，提出GenTO统一框架，通过训练扩散模型并引导拓扑分布，能为异构任务重用拓扑先验，保留结构多样性，获高性能方案，确立拓扑知识为超材料设计统一原则。
AI中文摘要：
结构化超材料的功能源于结构，通过拓扑设计来规划物理响应有巨大机会。但现有设计方法常针对个别问题，随着目标、约束和物理功能变化，利用拓扑知识进行有效且广泛适用设计的能力有限。本文引入生成拓扑优化（GenTO），这是一个统一框架，将学到的拓扑先验转化为可重复使用的设计引擎。GenTO在大型全序拓扑数据集上训练扩散模型，然后使用用户定义的物理目标和约束，将所得拓扑分布迭代引导至特定任务的高性能区域。这将优化对象从单个结构转变为适应任务的拓扑分布。在跨越热极值、多目标形态控制、性能目标的负泊松比设计和振动传输设计等拓扑设计问题中，GenTO为异构任务重用预训练的拓扑先验，保留结构多样性，并通过数值基准和实验验证获得高性能解决方案。这些结果将可重复使用的拓扑知识确立为有效且可扩展的结构化超材料设计的统一原则。
英文摘要：
Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design. However, existing design methods are often tailored to individual design problems, making limited use of topology knowledge for effective and broadly applicable design as objectives, constraints, and physical functions change. Here we introduce Generative Topology Optimization (GenTO), a unified framework that turns a learned topology prior into a reusable design engine. GenTO trains a diffusion model on a large full-order topology dataset and then iteratively steers the resulting topology distribution toward task-specific high-performing regions using user-defined physical objectives and constraints. This shifts the object of optimization from a single structure to a task-adapted topology distribution. Across topology design problems spanning thermal extremization, multi-objective morphology control, property-targeted auxetic design, and vibration transmission design, GenTO reuses pretrained topology priors for heterogeneous tasks, preserves structural diversity, and reaches high-performing solutions supported by numerical benchmarks and experimental validation. These results establish reusable topology knowledge as a unified principle for effective and scalable architected metamaterial design.
