The accelerated inverse design of complex material properties - such as identifying a material with a given stress-strain response over a nonlinear deformation path - holds great potential for addressing challenges from soft robotics to biomedical implants and impact mitigation. While machine learning models have provided such inverse mappings, they are typically restricted to linear target properties such as stiffness. To tailor the nonlinear response, we here show that video diffusion generative models trained on full-field data of periodic stochastic cellular structures can successfully predict and tune their nonlinear deformation and stress response under compression in the large-strain regime, including buckling and contact. Unlike commonly encountered black-box models, our framework intrinsically provides an estimate of the expected deformation path, including the full-field internal stress distribution closely agreeing with finite element simulations. This work has thus the potential to simplify and accelerate the identification of materials with complex target performance.

翻译：通过逆向设计加速复杂材料特性的识别——例如确定一种在非线性变形路径下具有给定应力-应变响应的材料——在软体机器人、生物医学植入物和冲击减缓等领域具有巨大潜力。尽管机器学习模型已能提供此类逆向映射，但它们通常局限于刚度等线性目标特性。为了定制非线性响应，我们在此展示，基于周期性随机胞状结构全场数据训练的视频扩散生成模型，能够成功预测并调控其在压缩大应变工况下的非线性变形与应力响应，包括屈曲和接触。与常见的黑箱模型不同，我们的框架能天然地提供预期变形路径的估计，包括与有限元模拟高度吻合的全场内部应力分布。因此，这项工作有望简化和加速具有复杂目标性能材料的识别过程。