NEUP CINR-21-24394: Computer vision and machine learning for microstructural qualification

Quantifying and understanding microstructure is a key driver for performance-based materials qualification. In this proposal, we gather well-curated data sets of microstructural images and apply computer vision and machine learning to build quantitative deep learning frameworks to accelerate and enable qualification of nuclear materials based on microstructural features.

Additional Info

Field	Value
Awarded Institution	Carnegie Mellon University
PI	Elizabeth Holm