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Conference Spotlight
2026 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
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NN Asks: Is the U.S. ready for nuclear construction to accelerate?
Craig Stover
Yes, but . . .
The United States is better positioned today for nuclear construction than it has been in decades. Some of that comes from the experience gained at Vogtle and V.C. Summer. I was part of the team that helped start the V.C. Summer project in 2008, and at that time we were trying to build a nuclear construction workforce from scratch. We learned a lot through that effort, and many of those lessons learned have since been studied, documented, and shared.
The nuclear industry is also benefiting from the wave of investment that started growing around 2020. Over the last five or six years, there has been a serious effort across the country to get ready for new nuclear builds. The U.S. government and the private sector are investing billions of dollars in new nuclear. Much of that work is happening before widespread commercial deployment contracts are signed. This is real, and we need to prepare.
Technical Session|Computational Methods, Artificial Intelligence, and Machine Learning
Saturday, April 6, 2024|1:35–2:55PM EDT|Engineering Services Building Room 122
Session Chair:
Luiz C. Aldeia Machado (Penn State University)
Alternate Chair:
Alexander S. Hauck (Penn State University)
Session Organizer:
Jonathan B. Balog (Penn State University)
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Machine Learning for Time-Series Prediction of the Cryogenic Moderator System in Oak Ridge National Laboratory's Spallation Neutron Source Facility
1:35–1:55PM EDT
Gavin McDuffee (Tennessee Technological Univ.), William Gurecky (Univ. Texas, Austin), Wesley C. Williams (ORNL), Xingang Zhao (ORNL)
Paper
ML-LIBS: Machine Learning-Based Spectra Predictions of Time-Dependent Lithium Emission Spectroscopy Imaging
1:55–2:15PM EDT
Lauren A. Kohler (NCSU), Jason P. Clifford (NCSU), Nursat Karim (NCSU), Sivanandan S. Harilal (PNNL), Elizabeth Kautz (NCSU), Xu Wu (NCSU)
Parameter Importances from Random Forest Machine Learning for Accident Tolerant Fuel Pool Boiling
2:15–2:35PM EDT
Eliot R. Ciuperca (Univ. Wisconsin, Madison), Juliana P. Duarte (Univ. Wisconsin, Madison), Bruno P. Serrao (Univ. Wisconsin, Madison)
A Preliminary Exploration into Using Mixture Density Networks to Compress Data in Monte Carlo Codes
2:35–2:55PM EDT
Eappen S. Nelluvelil (Univ. Colorado, Boulder), Anna Matsekh (LANL), Arvind Mohan (LANL), Mathew A. Cleveland (LANL), Alex Long (LANL)
