NEUP DECP-24-31370: Establishing the Predictive Credibility of Data-Driven Scientific Machine Learning for Nuclear Applications

The objective is to establish and enhance the predictive credibility of data-driven SciML for nuclear applications through rigorous uncertainty quantification of SciML models to establish confidence, as well as deep generative learning to address the data scarcity issue. The project will augment the applications of SciML in NE scientific computing and prepare the students for transformative solutions across various DOE missions through six Education and three Leadership thrusts areas.

Additional Info

Field	Value
Awarded Institution	North Carolina State University
PI	Xu Wu