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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Powering the future: How the DOE is fueling nuclear fuel cycle research and development
As global interest in nuclear energy surges, the United States must remain at the forefront of research and development to ensure national energy security, advance nuclear technologies, and promote international cooperation on safety and nonproliferation. A crucial step in achieving this is analyzing how funding and resources are allocated to better understand how to direct future research and development. The Department of Energy has spearheaded this effort by funding hundreds of research projects across the country through the Nuclear Energy University Program (NEUP). This initiative has empowered dozens of universities to collaborate toward a nuclear-friendly future.
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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