用扩散模型实现大尺度微结构重建，突破生成面积限制。
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects
- 基于去噪扩散模型的多尺度图像修复思路
- 生成结果与SPPARKS模拟的晶粒结构统计相似
- 适合需要大规模复杂微结构生成的研究者
基于仿真的微结构生成方法常受限于高内存占用、长计算时间以及复杂几何生成困难等问题。生成式机器学习模型可缓解这些挑战，但以往模型受限于固定生成区域大小。本文提出一种新方法，利用去噪扩散模型的图像修复能力，克服生成区域限制。实验表明，该方法生成的微结构在统计特性上与使用动力学蒙特卡洛模拟器SPPARKS生成的晶粒结构高度相似。
原文摘要 · Abstract (English)
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.
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