GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects
This addresses microstructure reconstruction for materials science, but it is incremental as it builds on existing diffusion models.
The paper tackled the problem of generating large-scale microstructures by overcoming the fixed generation area limitation of previous generative models, achieving statistical similarity to structures from a kinetic Monte Carlo simulator.
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.