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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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The RAIN scale: A good intention that falls short
Radiation protection specialists agree that clear communication of radiation risks remains a vexing challenge that cannot be solved solely by finding new ways to convey technical information.
Earlier this year, an article in Nuclear News described a new radiation risk communication tool, known as the Radiation Index, or, RAIN (“Let it RAIN: A new approach to radiation communication,” NN, Jan. 2025, p. 36). The authors of the article created the RAIN scale to improve radiation risk communication to the general public who are not well-versed in important aspects of radiation exposures, including radiation dose quantities, units, and values; associated health consequences; and the benefits derived from radiation exposures.
Workshop
Sunday, October 3, 2021|2:00–6:00PM EDT
Session Chair:
Xu Wu (NC State Univ.)
Student Producer:
William Dawn (NC State Univ.)
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that is the study of computer algorithms that improve automatically through experience (data). Deep Learning (DL) is a subset of ML that uses multi-layered neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others. Scientific Machine Learning (SciML), more specifically, consists of computational technologies that can be trained with scientific data to augment or automate human skills. ML has been very successful in areas such as computer vision, natural language processing, etc. But its application in scientific computing is relatively new, especially in Nuclear Engineering (NE). This workshop aims at augmenting the applications of AI/ML in scientific computing in NE, and promoting ML-based transformative solutions across various DOE missions.
This workshop includes presentations from five speakers. The topics are listed below:
1: Introduction, Uncertainty Quantification and Scientific Machine Learning, Dr. Xu Wu, Assistant Professor, North Carolina State University
2: NeuroEvolution Optimization with Reinforcement Learning, Dr. Majdi Radaideh, Research Scientist, Massachusetts Institute of Technology
3: A Machine Learning Approach for Scale Bridging in System-level Thermal-hydraulic Simulation, Dr. Han Bao, Computational Scientist, Idaho National Laboratory
4: Machine Learning Augmented Cross Section Evaluation, Dr. Massimiliano Fratoni, Xenel Distinguished Professor, University of California, Berkeley
5: Physics-Informed Machine Learning, Dr. Yang Liu, Nuclear Engineer, Argonne National Laboratory
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Attachment — MC2021_SciML_Workshop_Xu_Wu
Attachment — MC2021_SciML_Workshop_Majdi_Radaideh
Attachment — MC2021_SciML_Workshop_Han_Bao
Attachment — MC2021_SciML_Workshop_Massimiliano_Fratoni
Attachment — MC2021_SciML_Workshop_Yang_Liu
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