  Spider webs are incredible biological structures, comprising thin but strong
silk filament and arranged into complex hierarchical architectures with
striking mechanical properties (e.g., lightweight but high strength, achieving
diverse mechanical responses). While simple 2D orb webs can easily be mimicked,
the modeling and synthesis of 3D-based web structures remain challenging,
partly due to the rich set of design features. Here we provide a detailed
analysis of the heterogenous graph structures of spider webs, and use deep
learning as a way to model and then synthesize artificial, bio-inspired 3D web
structures. The generative AI models are conditioned based on key geometric
parameters (including average edge length, number of nodes, average node
degree, and others). To identify graph construction principles, we use
inductive representation sampling of large experimentally determined spider web
graphs, to yield a dataset that is used to train three conditional generative
models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics,
with sparse neighbor representation, 2) a discrete diffusion model with full
neighbor representation, and 3) an autoregressive transformer architecture with
full neighbor representation. All three models are scalable, produce complex,
de novo bio-inspired spider web mimics, and successfully construct graphs that
meet the design objectives. We further propose algorithm that assembles web
samples produced by the generative models into larger-scale structures based on
a series of geometric design targets, including helical and parametric shapes,
mimicking, and extending natural design principles towards integration with
diverging engineering objectives. Several webs are manufactured using 3D
printing and tested to assess mechanical properties.
