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Conference Spotlight
2026 Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear Science and Engineering
December 2025
Nuclear Technology
Fusion Science and Technology
November 2025
Latest News
Education and training to support Canadian nuclear workforce development
Along with several other nations, Canada has committed to net-zero emissions by 2050. Part of this plan is tripling nuclear generating capacity. As of 2025, the country has four operating nuclear generating stations with a total of 17 reactors, 16 of which are in the province of Ontario. The Independent Electricity System Operator has recommended that an additional 17,800 MWe of nuclear power be added to Ontario’s grid.
Technical Session|Sponsored by MCD
Tuesday, June 17, 2025|10:00–11:45AM CDT|Armitage
Session Chair:
Xu Wu
Alternate Chair:
Sebastian Schunert
Session Organizer:
Koroush Shirvan
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Prediction of Startup Excess Reactivity in TRIGA MK II Reactor: A Neural Network Approach
10:00–10:20AM CDT
Jeongwon Seo (Univ. Texas, Austin), William S. Charlton (Univ. Texas, Austin), Kevin T. Clarno (Univ. Texas, Austin)
Paper
PINN-Based Online Monitoring for Detection of Sensor Drift
10:20–10:40AM CDT
Jiyong Lee (KAIST), Jonghyun Kim (KAIST)
Intelligent Prediction and Uncertainty Quantification of Reactivity-Induced Power Excursions in Molten Salt Reactors
10:40–11:00AM CDT
Hui-Yu Hsieh (TAMU), Thabit M. Abuqudaira (TAMU), Pavel V. Tsvetkov (TAMU), Piyush Sabharwall (INL)
Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian Neural Networks
11:00–11:20AM CDT
Aidan J. Furlong (NCSU), Xingang Zhao (Univ. Tennessee, Knoxville), Robert K. Salko (ORNL), Xu Wu (NCSU)
Epistemic Uncertainty in Deep Learning Models and Its Application for Model Performance Improvement
11:20–11:40AM CDT
Junyong Bae (Ulsan Nat'l Institute Science and Technology), Seung Jun Lee (Ulsan Nat'l Institute Science and Technology)
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