Recent advances in generative modeling, namely Diffusion models, have
revolutionized generative modeling, enabling high-quality image generation
tailored to user needs. This paper proposes a framework for the generative
design of structural components. Specifically, we employ a Latent Diffusion
model to generate potential designs of a component that can satisfy a set of
problem-specific loading conditions. One of the distinct advantages our
approach offers over other generative approaches, such as generative
adversarial networks (GANs), is that it permits the editing of existing
designs. We train our model using a dataset of geometries obtained from
structural topology optimization utilizing the SIMP algorithm. Consequently,
our framework generates inherently near-optimal designs. Our work presents
quantitative results that support the structural performance of the generated
designs and the variability in potential candidate designs. Furthermore, we
provide evidence of the scalability of our framework by operating over voxel
domains with resolutions varying from 323 to 1283. Our framework can be
used as a starting point for generating novel near-optimal designs similar to
topology-optimized designs