Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models

Abstract

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.

21 pages, 3 figures

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• Investigation on optimal microstructure of dual-phase steel with high strength and ductility by machine learning
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• Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers
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• Reinforcement learning for inverse structural design and rapid laser cutting of kirigami prototypes