Automated Defect Identification for Tristructural Isotropic Fuels

B-3: 3D Computer Vision and Machine Learning for Porosity Analysis in Additive Manufacturing

Combining Limited Image and Tabular Data to Understand Failure Modes in Metals

Computer Vision Applications in Materials Science and Engineering

Establishing PSP Relationships with Microstructure Features Quantified Using Machine Learning

FeCrAl Alloy Design Utilizing Literature, Experiments, High Throughput Characterization, and Machine Learning

Machine Learning Enabled Reproducible Data Analysis for Electron Microscopy

Materials Data Science for Reliability: Data Handling

Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN

Neighborhood Maps for Discovery of Novel Materials in Reduced Dimensions Using Machine Learning

Polycrystal Graph Neural Network

Process-Structure-Property Relationships from Variational Autoencoders

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