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Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
2021 ANS Winter Meeting and Technology Expo
November 30–December 3, 2021
Washington, DC|Washington Hilton
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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“Empowering Women to Succeed” webinar takes place tomorrow
The #AtomicAllies (ANS, North American Young Generation in Nuclear, U.S. Women in Nuclear, and the Nuclear Energy Institute) will present a free panel discussion, titled “Empowering Women to Succeed,” on September 22, 1:00–2:15 p.m. EDT.
Register now to hear from some of the brightest stars of the industry as they share their keys to success.
Tuesday, October 5, 2021|9:40–11:20AM (10:40AM–12:20PM EDT)|Salon C
Joshua Hykes (Studsvik Scandpower)
Benoit Forget (MIT)
William Wieselquist (ORNL)
To access paper attachments, you must be logged in and registered for the meeting.
Register NowLog In
Physics-Informed Deep Learning Neural Network Solution to the Neutron Diffusion Model
Mohamed H. Elhareef (Virginia Commonwealth Univ.), Zeyun Wu (Virginia Commonwealth Univ.), Yu Ma (Sun Yat-sen Univ.)
Improving Whole-Core Calculations by Bayesian Inference From Single-Assembly Measured Reactivity Weights
P.-L. Alzieu (CEA), G. Truchet (CEA), J. Tommasi (CEA)
Investigation Into the Use of Machine Learning Assisted Prediction of Nodal Parameters for Reduced Order Neutronic Simulation Models
Madhumitha Ravichandran (MIT), Cole A. Gentry (ORNL), Matteo Bucci (MIT)
Improved Rational Approximation for Spatially-Dependent Resonance Self-Shielding in CASMO5
Rodolfo Ferrer (Studsvik Scandpower), Joshua Hykes (Studsvik Scandpower)
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