Machine learning based quantitative characterization of microstructures

Gorynski, Claudia; Frei, Max; Kruis, Frank Einar; Winterer, Markus

doi:10.1016/j.actamat.2023.119106

Cited by 6 publications

(1 citation statement)

References 79 publications

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“…Scientific machine learning (ML), including deep learning methods, has emerged in recent years as a powerful family of computational tools that complement and extend the capabilities of traditional computational materials science toolkit [11,12]. In the domain of microstructure characterization, convolutional neural networks (CNN) and related methods have been successfully applied to various image analysis tasks such as feature (grain size, aspect ratio, spacing, etc) extraction and quantification, microstructure classification, image denoising and super-resolution, defect detection, semantic image segmentation, and even 2D and 3D microstructure generation [13][14][15][16][17][18][19][20]. As such, CNN-based deep learning methods are becoming part of the standard toolkit for static image processing for microscopy experiments.…”

Section: Introductionmentioning

confidence: 99%

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Fan,

Hitt,

Tang

et al. 2024

Mach. Learn.: Sci. Technol.

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Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

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