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Found 5 papers, 1 papers with code
Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes.
Overview: Computer vision and machine learning for microstructural characterization and analysis
The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties.
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively.
A comparative study of feature selection methods for stress hotspot classification in materials
The first step in constructing a machine learning model is defining the features of the data set that can be used for optimal learning.
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure.
