Open Access
Issue
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
Article Number 07002
Number of page(s) 8
Section High Performance Computing for Nuclear Data Processing – Benchmarking
DOI https://doi.org/10.1051/epjconf/202430207002
Published online 15 October 2024
  1. Forrest, R. A. Nuclear Science and Data Needs for Advanced Nuclear Systems. Energy Procedía 2011, 7, 540–552. https://doi.Org/10.1016/j.egypro.2011.06.075. [CrossRef] [Google Scholar]
  2. Bernstein, L. A.; Brown, D.; Hurst, A. M.; Kelley, J. H.; Kondev, F. G.; McCutchan, E. A.; Nesaraja, C. D.; Slaybaugh, R.; Sonzogni, A. Nuclear Data Needs and Capabilities for Applications Whitepaper, LLNL-CONF-676585; Lawrence Livermore National Lab. (LLNL), Livermore, Ca (United States), 2015. https://www.osti.gov/biblio/1229857 (accessed 2024-02-24). [Google Scholar]
  3. Bernstein, L. A.; Brown, D. A.; Koning, A. J.; Rearden, B. T.; Romano, C. E.; Sonzogni, A. A.; Voyles, A. S.; Younes, W. Our Future Nuclear Data Needs. Annu. Rev. Nucl. Part. Sci. 2019, 69 (1), 109–136. https://doi.org/10.1146/annurev-nucl-101918-023708. [Google Scholar]
  4. Kolos, K.; Sobes, V.; Vogt, R.; Romano, C. E.; Smith, M. S.; Bernstein, L. A.; Brown, D. A.; Burkey, M. T.; Danon, Y.; Elsawi, M. A.; Goldblum, B. L.; Heilbronn, L. H.; Hogle, S. L.; Hutchinson, J.; Loer, B.; McCutchan, E. A.; Mumpower, M. R.; O’Brien, E. M.; Percher, C.; Peplowski, P. N.; Ressler, J. J.; Schunck, N.; Thompson, N. W.; Voyles, A. S.; Wieselquist, W.; Zerkle, M. Current Nuclear Data Needs for Applications. Phys. Rev. Res. 2022, 4 (2), 021001. https://doi.org/10.1103/PhysRevResearch.4.021001. [Google Scholar]
  5. Zerkin, V. V.; Pritychenko, B. The Experimental Nuclear Reaction Data (EXFOR): Extended Computer Database and Web Retrieval System. Nucl. Instrum. Methods Phys. Res. Sect. Accel. Spectrometers Detect. Assoc. Equip. 2018, 888, 31–43. https://doi.org/10.1016/j.nima.2018.01.045. [Google Scholar]
  6. Rev. Mod. Phys. 30, 257 (1958) - R-Matrix Theory of Nuclear Reactions. https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.30.257 (accessed 2024-0126). [Google Scholar]
  7. Vicente-Valdez, P.; Bernstein, L.; Fratoni, M. Nuclear Data Evaluation Augmented by Machine Learning. Ann. Nucl. Energy 2021, 163, 108596. https://doi.org/10.1016/j.anucene.2021.108596. [CrossRef] [Google Scholar]
  8. Brown, D. A.; Chadwick, M. B.; Capote, R.; Kahler, A. C.; Trkov, A.; Herman, M. W.; Sonzogni, A. A.; Danon, Y.; Carlson, A. D.; Dunn, M.; Smith, D. L.; Hale, G. M.; Arbanas, G.; Arcilla, R.; Bates, C. R.; Beck, B.; Becker, B.; Brown, F.; Casperson, R. J.; Conlin, J.; Cullen, D. E.; Descalle, M.-A.; Firestone, R.; Gaines, T.; Guber, K. H.; Hawari, A. I.; Holmes, J.; Johnson, T. D.; Kawano, T.; Kiedrowski, B. C.; Koning, A. J.; Kopecky, S.; Leal, L.; Lestone, J. P.; Lubitz, C.; Márquez Damián, J. I.; Mattoon, C. M.; McCutchan, E. A.; Mughabghab, S.; Navratil, P.; Neudecker, D.; Nobre, G. P. A.; Noguere, G.; Paris, M.; Pigni, M. T.; Plompen, A. J.; Pritychenko, B.; Pronyaev, V. G.; Roubtsov, D.; Rochman, D.; Romano, P.; Schillebeeckx, P.; Simakov, S.; Sin, M.; Sirakov, I.; Sleaford, B.; Sobes, V.; Soukhovitskii, E. S.; Stetcu, I.; Talou, P.; Thompson, I.; van der Marck, S.; Welser-Sherrill, L.; Wiarda, D.; White, M.; Wormald, J. L.; Wright, R. Q.; Zerkle, M.; Žerovnik, G.; Zhu, Y. ENDF/B-VIIJ.0: The 8th Major Release of the Nuclear Reaction Data Library with CIELO-Project Cross Sections, New Standards and Thermal Scattering Data. Nucl. Data Sheets 2018, 148, 1–142. https://doi.org/10.1016Zj.nds.2018.02.001. [CrossRef] [Google Scholar]
  9. R. E. MacFarlane; D. W., Muir; R. M., Boicourt; A. C., Kahler; J. L., Conlin; W., Haeck. The NJOYNuclear Data Processing System, Version 2016; LA-UR-17-20093; Los Alamos National Laboratory. [Google Scholar]
  10. Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks | Request PDF. https://www.researchgate.net/publication/347008151_Frequency_Principle_Fourier_Analysis_Sheds_Light_on_Deep_Neural_Networks (accessed 2024-03-18). [Google Scholar]
  11. Cai, W.; Li, X.; Liu, L. A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems. SIAM J. Sci. Comput. 2020, 42 (5), A3285–A3312. https://doi.org/10.1137/19M1310050. [Google Scholar]
  12. A. Trkov; M., Herman; D. A., Brown; N., Holden; G., Hedstrom. ENDF-6 Formats Manual, Data Formats and Procedures for the Evaluated Nuclear Data Files ENDF/B-VI, ENDF/B-VII andENDF/B-VIII; BNL-203218-2018-INRE; National Nuclear Data Center, Brookhaven National Laboratory, 2018. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.