Promoting Optimal Sparse Sensing and Sparse Learning for Nuclear Digital Twins

This project will address the efficient use of limited experimental data available for nuclear digital twin (NDT) training and demonstration. This involves developing sparse data reconstruction methods and using NDT models to define sensor requirements (location, number, accuracy) for the design of demonstration experiments. NDTs should leverage 1) sparse sensing for identifying optimal locations and the minimal set of required sensors and 2) sparse learning and recovery of full maps of responses of interest for stronger prediction, diagnostics, and prognostics capabilities.