标题： DiffuMeta：通过扩散变换器进行超材料逆向设计的代数语言模型 显示英文标题

标题： DiffuMeta: Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers

摘要： 生成式机器学习模型通过捕捉复杂的结构-性能关系，彻底改变了材料发现，但将这些方法扩展到三维超材料的逆向设计仍受到计算复杂性和由于缺乏表达性表示而未被充分探索的设计空间的限制。 在这里，我们提出了DiffuMeta，这是一种生成框架，结合了扩散变换器和一种新颖的代数语言表示，将三维几何编码为数学句子。 这种紧凑且统一的参数化方法涵盖了多种拓扑结构，同时使变换器可以直接应用于结构设计。 DiffuMeta利用扩散模型生成具有精确目标应力-应变响应的新壳体结构，在大变形下考虑屈曲和接触，并通过生成多样解来解决固有的多对一映射问题。 独特的是，我们的方法能够同时控制多个机械目标，包括超出训练领域线性和非线性响应。 制造结构的实验验证进一步证实了我们方法在加速具有定制特性的超材料和结构设计中的有效性。 显示英文摘要

摘要： Generative machine learning models have revolutionized material discovery by capturing complex structure-property relationships, yet extending these approaches to the inverse design of three-dimensional metamaterials remains limited by computational complexity and underexplored design spaces due to the lack of expressive representations. Here, we present DiffuMeta, a generative framework integrating diffusion transformers with a novel algebraic language representation, encoding 3D geometries as mathematical sentences. This compact, unified parameterization spans diverse topologies while enabling direct application of transformers to structural design. DiffuMeta leverages diffusion models to generate novel shell structures with precisely targeted stress-strain responses under large deformations, accounting for buckling and contact while addressing the inherent one-to-many mapping by producing diverse solutions. Uniquely, our approach enables simultaneous control over multiple mechanical objectives, including linear and nonlinear responses beyond training domains. Experimental validation of fabricated structures further confirms the efficacy of our approach for accelerated design of metamaterials and structures with tailored properties.