  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.
