Digital polycrystalline microstructure generation using diffusion probabilistic models

doi:10.1016/j.mtla.2023.101976

Digital polycrystalline microstructure generation using diffusion probabilistic models

2024-03-01

6

PRE

AI

P

Patxi Fernandez-Zelaia *

DOI：10.1016/j.mtla.2023.101976

Original

Original request for help

Save

Abstract

En 中文

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. This work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

Keywords:

Microstructure

Machine learning

Generative modeling

ICME

Digital polycrystalline microstructure generation using diffusion probabilistic models

Abstract

Journal

Organization