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 gene...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Applied Computing and Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197425000308 |
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| Summary: | 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. |
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| ISSN: | 2590-1974 |