Microstructure reconstruction using diffusion-based generative models

AbstractThis 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 reconstructiondiffusion modeldenoising diffusion probabilistic modelneural networkcomposite materials Data availability statementNo data was used for the research described in the article.Additional informationFundingThis material is based upon work supported by the Air Force Office of Scientific Research under award number FA2386-22-1-4001 and the Institute of Engineering Research at Seoul National University. The authors are grateful for their support.