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GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects
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DOI：10.1016/j.actamat.2025.120784.png)
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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 away around these issues, but they have previously been limited by the fixed size of their generation area. We present anew 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.
Keyword:
Kinetic Monte Carlo
Generative deep learning
Microstructure reconstruction
Multiscale
期刊
IF:
9.3
论文数:
2.0W
被引数:
12.9W
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