{"id":1376,"date":"2025-09-21T12:37:15","date_gmt":"2025-09-21T16:37:15","guid":{"rendered":"https:\/\/ne.ncsu.edu\/artisans\/?page_id=1376"},"modified":"2025-09-21T21:01:16","modified_gmt":"2025-09-22T01:01:16","slug":"others","status":"publish","type":"page","link":"https:\/\/ne.ncsu.edu\/artisans\/others\/","title":{"rendered":"Others"},"content":{"rendered":"\n

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Workshop Series on “Scientific Machine Learning for Nuclear Engineering Applications”<\/strong><\/span><\/p>\n\n\n\n

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SciML Workshop at 2021 M&C Conference (virtual on Zoom)<\/strong><\/span><\/td>\n<\/tr>\n
Speakers<\/span><\/strong><\/td>\nOrganizations<\/span><\/strong><\/td>\nPresentation Titles<\/span><\/strong><\/td>\n<\/tr>\n
Dr. Xu Wu<\/td>\nAssistant Professor, Department of Nuclear Engineering, North Carolina State University<\/td>\nUncertainty Quantification and Scientific Machine Learning (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Majdi Radaideh<\/td>\nResearch Scientist, Department of Nuclear Science and Engineering, Massachusetts Institute of Technology<\/td>\nNeuroEvolution Optimization with Reinforcement Learning (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Han Bao<\/td>\nComputational Scientist, Department of Digital Reactor Technology & Development, Idaho National Laboratory<\/td>\nA Machine Learning Approach for Scale Bridging in System-level Thermal-hydraulic Simulation (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Massimiliano Fratoni<\/td>\nXenel Distinguished Professor, Department of Nuclear Engineering, University of California, Berkeley<\/td>\nMachine Learning Augmented Cross Section Evaluation (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Yang Liu<\/td>\nNuclear Engineer, Nuclear Engineering Division, Argonne National Laboratory<\/td>\nPhysics-Informed Machine Learning (slides<\/a>)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n\n
SciML Workshop at 2022 PHYSOR Conference (May 15, 2022, Pittsburgh, PA, USA) <\/strong>(Introduction slides<\/a>)<\/span><\/td>\n<\/tr>\n
Speakers<\/span><\/strong><\/td>\nOrganizations<\/span><\/strong><\/td>\nPresentation Titles<\/span><\/strong><\/td>\n<\/tr>\n
Dr. Som Dhulipala<\/td>\nComputational Scientist, Department of Computational Mechanics and Materials, Idaho National Laboratory<\/td>\nActive learning for computational simulations: Application to TRISO fuel failure analysis (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Ayman Hawari<\/td>\nDistinguished Professor, Department of Nuclear Engineering, North Carolina State University<\/td>\nDevelopment of Neural Thermal Scattering Modules for Data Representation and Applications (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Linyu Lin<\/td>\nPostdoc, Department of Instrumentation, Controls & Data Science, Idaho National Laboratory<\/td>\nDevelopment of a Nearly Autonomous Management and Control System for Advanced Reactors (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Vladimir Sobes<\/td>\nAssistant Professor, Department of Nuclear Engineering, University of Tennessee, Knoxville<\/td>\nApplications of AI\/ML from Nuclear Data to Reactor Design (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Justin Watson<\/td>\nAssociate Professor, Department of Materials Science and Engineering, University of Florida<\/td>\nPrediction of PWR Pin Powers using Convolutional Neural Networks (slides<\/a>)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n
SciML Workshop at 2023 M&C Conference (August 13, 2023 Niagara Falls, Ontario, Canada) <\/strong>(Introduction slides<\/a>)<\/span><\/td>\n<\/tr>\n
Speakers<\/span><\/strong><\/td>\nOrganizations<\/span><\/strong><\/td>\nPresentation Titles<\/span><\/strong><\/td>\n<\/tr>\n
Dr. Xu Wu<\/td>\nAssistant Professor, Department of Nuclear Engineering, North Carolina State University<\/td>\nEverything You Need to Know About Neural Networks (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Brendan Kochunas<\/td>\nAssistant Professor, Department of Nuclear Engineering and Radiological Sciences, University of Michigan – Ann Arbor<\/td>\nHybrid Machine Learning Techniques in Microreactor Control (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Stephen Lam<\/td>\nAssistant Professor, Department of Chemical Engineering, University of Massachusetts Lowell<\/td>\nML-Enhanced Interrogation of Complex Material Structures and Properties (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Hany Abdel-Khalik<\/td>\nProfessor, Department of Nuclear Engineering, Purdue University<\/td>\nInferring with Intelligence: A Nuclear Perspective (slides<\/a>)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n\n
SciML Workshop at 2024 PHYSOR Conference (April 21, 2024, San Francisco, CA, USA) <\/strong>(Introduction slides<\/a>)<\/span><\/td>\n<\/tr>\n
Speakers<\/span><\/strong><\/td>\nOrganizations<\/span><\/strong><\/td>\nPresentation Titles<\/span><\/strong><\/td>\n<\/tr>\n
Dr. William L Gurecky<\/td>\nR&D associate, Nuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory<\/td>\nLatent Neural Controlled Differential Equations for Time Series Forecasting (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Jean Ragusa<\/td>\nProfessor, Department of Nuclear Engineering, Texas A&M University<\/td>\nPhysics-informed, Data-driven Reduced-order Models for Nuclear Applications (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Mohammad Abdo<\/td>\nSenior staff scientist, Idaho National Laboratory<\/td>\nSparse Sensing and Sparse Learning in Nuclear Digital Twins: Applications (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Thomas Gruenwald<\/td>\nExecutive Vice President and Co-Founder, Blue Wave AI Labs<\/td>\nNeutron Flux Measurement and Thermal Limits Modeling: Impact on Operational Performance (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Diego Mandelli<\/td>\nR&D Scientist, Idaho National Laboratory<\/td>\nCausal Inference: A Bridge Between Simulation Models and Observed Phenomena (slides<\/a>)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n
SciML Workshop at 2025 M&C Conference (April 27, 2025, Denver, CO, USA)<\/strong> (Introduction slides<\/a>)<\/span><\/td>\n<\/tr>\n
Speakers<\/span><\/strong><\/td>\nOrganizations<\/span><\/strong><\/td>\nPresentation Titles<\/span><\/strong><\/td>\n<\/tr>\n
Dr. Xingang Zhao<\/td>\nAssistant Professor, Department of Nuclear Engineering, University of Tennessee Knoxville<\/td>\nDigital Engineering for the Future of Nuclear Power: A CROSS-cutting Approach to Safety and Performance (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Juliana Duarte<\/td>\nAssistant Professor, Department of Nuclear Engineering and Engineering Physics, University of Wisconsin – Madison<\/td>\nUse of AI to improve models in safety and risk analysis (slides<\/a>)<\/td>\n<\/tr>\n
Dr. Jason Hou<\/td>\nAssociate Professor in the Department of Nuclear Engineering, North Carolina State University<\/td>\nPrototyping of a Machine Learning Enabled Burnup Measurement Capability for High-Temperature Reactors (slides<\/a>)<\/td>\n<\/tr>\n
Mr. Joseph Farmer<\/td>\nPhD student, Department of Aerospace and Mechanical Engineering, University of Notre Dame<\/td>\nGeometric deep learning for science and engineering (on behalf of Dr. Ryan McClarren) (slides<\/a>)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

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