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Found 4 papers, 2 papers with code
Neural message passing for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution
However, recent advances in deep learning provide a promising alternative to traditional experimental and physics-based methods for understanding this phenomenon.
Optimal transportation of grain boundaries: A forward model for predicting migration mechanisms
In order to test the minimum distance hypothesis, optimal displacement patterns predicted on the basis of a regularized OT based forward model are compared to molecular dynamics (MD) GB migration data for a variety of GB types and temperatures.
Materials Science
Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data
Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images.
Instance Segmentation
Semantic Segmentation
+1
Materials Science
Image and Video Processing
Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance.
