NEUP CINR-25-34795: Digital Twins for 4D Spatiotemporal Prediction of Advanced Reactors

This project will develop digital twins for 4D spatiotemporal prediction in advanced reactors by combining direct 3D conformal sensor printing for real-time data acquisition and a novel "data+physics"-based deep learning framework for rapid predictive simulation and live mode-data fusion. The digital-twin model aims to significantly enhance the design and optimization, predictive maintenances, and operational efficiency and safety measures of advanced reactors.

Additional Info

Field	Value
Awarded Institution	University of Notre Dame
PI	Yanliang Zhang