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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Li Cheng, Bin Zhong, Huayun Shen, Zehua Hu, Baiwen Li
Nuclear Science and Engineering | Volume 194 | Number 1 | January 2020 | Pages 44-55
Technical Paper | doi.org/10.1080/00295639.2019.1650520
Articles are hosted by Taylor and Francis Online.
We propose an improved algorithm of generating scattering matrices based on the Monte Carlo method. The new algorithm can greatly improve convergence compared to the traditional approach of the collision estimator. The formula for estimating statistical errors in the new algorithm is given. How the new algorithm benefits the convergence without investing large neutron samples is analyzed, and we also point out that with properly partitioned energy groups, the precision of scattering matrices can get close to that of total scattering cross sections. The new algorithm has been implemented in the neutron transport code NPTS and validated with a number of critical benchmark problems.