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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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NRC’s hybrid AI workshop coming up
The Nuclear Regulatory Commission will host a hybrid public workshop on September 24 from 9 a.m.-5 p.m. Eastern time to discuss its activities for the safe and secure use of artificial intelligence in NRC-regulated activities.
Patrick Behne, Jan Vermaak, Jean Ragusa
Nuclear Science and Engineering | Volume 197 | Number 2 | February 2023 | Pages 233-261
Technical Paper | doi.org/10.1080/00295639.2022.2112901
Articles are hosted by Taylor and Francis Online.
This work presents a data-driven, projection-based parametric reduced-order model (ROM) for the neutral particle radiation transport (linear Boltzmann transport) equation. The ROM utilizes the method of snapshots with proper orthogonal decomposition. The novelty of the work is in the detailed proposal to exploit the parametrically affine transport operators to intrusively, yet efficiently, build the reduced transport operators in real time in a matrix-free manner compatible with sweep-based transport solvers. This affine-based ROM is applied to one-dimensional (1-D), two-dimensional (2-D), and 2-D multigroup transport benchmarks and is found to significantly outperform less intrusive ROMs in terms of speed for a desired accuracy level. The ROM has an 18.2 to 89.4 speedup with an error range of 0.0002% to 0.01% for the 1-D benchmark, a 1120× to 4870× speedup with an error range of 0.0009% to 0.01% for the 2-D benchmark, and a 54 600× to 399 800× speedup with an error range of 0.00022% to 0.01% for the multigroup 2-D benchmark. Even higher speedups are expected for three-dimensional multigroup transport problems.