6 citations · 6 across the 2 of their papers we have counts for
2 papers
stat.CO2022
Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path Forward to Digital Twin Enabling Simulation for Accident Tolerant Fuel
Kazuma Kobayashi, James Daniell, Shoaib Usman +2
The Gaussian Process (GP)-based surrogate model has the inherent capability of capturing the anomaly arising from limited data, lack of data, missing data, and data inconsistencies…
stat.AP2022★ 6 cited
Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction
James Daniell, Kazuma Kobayashi, Ayodeji Alajo +1
The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can…