NEUP CINR-25-34546: Physics-based Machine Learning Aided Multimodal NDT to Detect Internal Abnormalities in Sealed SNF Canisters

This project aims to develop a multimodal non-destructive testing (NDT) framework with hybrid data and physics-based machine learning (ML) algorithms to detect and quantify multiple internal abnormalities at-once in sealed spent nuclear fuel (SNF) canisters.

Additional Info

Field	Value
Awarded Institution	University of Southern California
PI	Bora Gencturk