NEUP CINR-24-31998: AI to Guide Sorption Data Acquisition and Assimilation into Uncertainty Quantifications for the Nuclear Waste Disposal Performance Assessment

The objective of this project is to develop machine learning (ML) and AI toolsets to effectively expand the global sorption database – the datasets collected by multiple institutions around the world – and to assimilate these datasets into the uncertainty quantification (UQ) in the performance assessment (PA) of nuclear waste repositories.

Additional Info

Field	Value
Awarded Institution	Massachusetts Institute of Technology
PI	Haruko Wainwright