.png){kind=icon}
.png){kind=icon}
Return
Microstructure reconstruction using diffusion-based generative models
.png){kind=icon}
OA
AI
DOI：10.1080/15376494.2023.2198528.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a microstructure reconstruction framework with denoising diffusion models for the first time. The novelty and strength of the proposed model lie in its universality and generality for the microstructure characterization and reconstruction (MCR) that can be applied to various types of composite materials. The applicability of the diffusion-based models is validated with several types of microstructures (e.g., polycrystalline alloy, carbonate, ceramics, copolymer, fiber composite, etc.) that have different morphological characteristics. Moreover, an implicit probabilistic model (which yields non-Markovian diffusion processes) is formulated to accelerate the sampling process, thereby controlling the computational cost considering the practicability and reliability.
Keywords:
microstructure reconstruction
diffusion model
denoising diffusion probabilistic model
neural network
composite materials
Journal
IF:
0
Papers:
4.7K
Citations:
1.4W