DESIGNING MECHANICAL META-MATERIALS BY LEARNING EQUIVARIANT FLOWS

Mechanical meta-materials are porous solids whose geometric structure results in exotic nonlinear mechanical behaviors that are not typically achievable via homogeneous materials. We show how to drastically expand the design space of a class of mechanical meta-materials known as cellular solids, by generalizing beyond translational symmetry of a unit pore cell. This is made possible by transforming a reference geometry according to a divergence free flow that is parameterized by a neural network and equivariant under the relevant symmetry group. We show how to construct flows equivariant to the space groups, despite the fact that these groups are not compact. Coupling this flow with a differentiable nonlinear mechanics simulator allows us to represent a much richer set of cellular solids than was previously possible. These materials can be optimized to exhibit desirable mechanical properties such as negative Poisson's ratios or to match target stress-strain curves. We validate simulated mechanical behaviors of these new designs against fabricated real-world prototypes. We find that designs with higher-order symmetries can exhibit a wider range of behaviors.