Metamaterials, synthetic materials with customized properties, have emerged as a promising field due to advancements in additive manufacturing. These materials derive unique mechanical properties from their internal lattice structures, which are often composed of multiple materials that repeat geometric patterns. While traditional inverse design approaches have shown potential, they struggle to map nonlinear material behavior to multiple possible structural configurations. This paper presents a novel framework leveraging video diffusion models, a type of generative artificial Intelligence (AI), for inverse multi-material design based on nonlinear stress-strain responses. Our approach consists of two key components: (1) a fields generator using a video diffusion model to create solution fields based on target nonlinear stress-strain responses, and (2) a structure identifier employing two UNet models to determine the corresponding multi-material 2D design. By incorporating multiple materials, plasticity, and large deformation, our innovative design method allows for enhanced control over the highly nonlinear mechanical behavior of metamaterials commonly seen in real-world applications. It offers a promising solution for generating next-generation metamaterials with finely tuned mechanical characteristics.

翻译：超材料作为具有定制化特性的合成材料，随着增材制造技术的发展已成为前景广阔的研究领域。这类材料通过其内部晶格结构获得独特的力学性能，这些结构通常由重复几何图案的多种材料构成。尽管传统的逆设计方法已展现出潜力，但在将非线性材料行为映射至多种可能的结构构型方面仍面临挑战。本文提出了一种创新框架，利用视频扩散模型（一种生成式人工智能）实现基于非线性应力-应变响应的多材料逆设计。我们的方法包含两个核心组件：（1）采用视频扩散模型的场量生成器，根据目标非线性应力-应变响应创建解场；（2）运用两个UNet模型的结构识别器，用于确定相应的多材料二维设计方案。通过引入多材料、塑性及大变形等因素，这一创新设计方法能够增强对实际应用中常见超材料高度非线性力学行为的调控能力，为生成具有精细调控力学特性的新一代超材料提供了前景广阔的解决方案。