GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917947335770112 |
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| author | Hoffman, Nathan Diniz, Cashen Liu, Dehao Rodgers, Theron Tran, Anh Fuge, Mark |
| author_facet | Hoffman, Nathan Diniz, Cashen Liu, Dehao Rodgers, Theron Tran, Anh Fuge, Mark |
| contents | Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. We present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects Hoffman, Nathan Diniz, Cashen Liu, Dehao Rodgers, Theron Tran, Anh Fuge, Mark Graphics Materials Science Computer Vision and Pattern Recognition Machine Learning Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. We present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS. |
| title | GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects |
| topic | Graphics Materials Science Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.04776 |