Open Access
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
Article Number 06012
Number of page(s) 8
Section Advanced Modelling and Simulation
Published online 22 February 2021
  1. A. Barreau. “Burn-up Credit Criticality Benchmark.” Technical Report Phase, IID, OECDNEA (2006). [Google Scholar]
  2. U. Grundmann et al. “DYN3D version 3.2 - code for calculation of transients in light water reactors (LWR) with hexagonal or quadratic fuel elements - description of models and methods -.” Wissenschaftlich-Technische Berichte (2019). [Google Scholar]
  3. R. Zivanovic and P. Bokov. “Cross-section parameterization of the pebble bed modular reactor using the dimension-wise expansion model.” Annals of Nuclear Energy, volume 37, pp. 1763–1773 (2010). [Google Scholar]
  4. J. Dufek. “Building the nodal nuclear data dependences in a many-dimensional state-variable space.” Annals of Nuclear Energy, volume 38(7), pp. 1569 – 1577 (2011). [Google Scholar]
  5. D. Botes and P. M. Bokov. “Polynomial interpolation of few-group neutron cross sections on sparse grids.” Annals of Nuclear Energy, volume 64, pp. 156 – 168 (2014). [Google Scholar]
  6. C. Sanchez et al. “Optimization of multidimensional cross-section tables for few-group core calculations.” Annals of Nuclear Energy, volume 69, pp. 226 – 237 (2014). [Google Scholar]
  7. E. Szames, K. Ammar, D. Tomatis, and J. Martinez. “Few-group cross sections library compression by artificial neural networks.” Submitted to proceding of the conference: Physics of reactor conference, Cambridge, United Kingdom (2019). [Google Scholar]
  8. H. L. Thi et al. “Use cases of Tucker decomposition method for reconstruction of neutron macroscopic cross-sections.” Annals of Nuclear Energy, volume 109, pp. 284 – 297 (2017). [Google Scholar]
  9. B. Scholkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA (2001). [Google Scholar]
  10. G. Wahba. Spline Models for Observational Data. Society for Industrial and Applied Mathematics, Philadelphia (1990). [Google Scholar]
  11. D. Wu. “Pool-Based Sequential Active Learning for Regression.” IEEE transactions on neural networks and learning systems (2018). [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.