3D clay microstructure synthesis using Denoising Diffusion Probabilistic Models

This work is concerned with the challenging task of generating 3D-consistent binary microstructures of heterogeneous clay materials. We leverage denoising diffusion probabilistic models (DDPMs) to do so and show that DDPMs outperform two classical generative adversarial networks (GANs) for a 2D generation task. Next, our experiments demonstrate that our DDPMs can produce high-quality, diverse realizations that well capture the spatial statistics of two distinct clay microstructures. Moreover, we show that DDPMs can be implicitly trained to generate porosity-conditioned samples. To the best of our knowledge, this is the first study that addresses clay microstructure generation with DDPMs. • We demonstrate the use of Denoising Diffusion Probabilistic Models (DDPMs) for the 3D generation of complex binary microstructures. • We show that, in contrast to the considered GANs, our trained DDPMs can generate high-quality images of complex clay structures such as compacted illite and Boom Clay materials. • We propose a conditional DDPM variant that can produce total-porosity-conditioned realizations.