Open Access
| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 11 | |
| Section | Deterministic Transport Codes: Algorithms, HPC & GPU | |
| DOI | https://doi.org/10.1051/epjconf/202430202006 | |
| Published online | 15 October 2024 | |
- A. Weiser, E. Zarantonello, Mathematics of computation (1988) [Google Scholar]
- S. Sánchez-Cervera, N. García-Herranz, J. Herrero, O. Cabellos, Annals of Nuclear Energy 69, 226 (2014) [Google Scholar]
- V. Zimin, A. Semenov, Annals of Nuclear Energy (2005) [Google Scholar]
- P. Bokov, Annals of Nuclear Energy (2009) [Google Scholar]
- D. Botes, P. Bokov, Annals of Nuclear Energy (2013) [Google Scholar]
- N. Martin, M. Riedmann, J. Bigot, Latest Developments in the ARTEMIS TM Core Simulator for BWR Steady-state and Transient Methodologies, in MC2017 (2017) [Google Scholar]
- E. Szames, D. Tomatis, K. Ammar, J. Martinez, Reconstruction of few-group homogenized cross section by kernel method and active learning, in M and C 2019 (Portland, United States, 2019), https://hal.science/hal-02411076 [Google Scholar]
- K. Tan, Z. Liu, C. Wei, M. Liu, Annals of Nuclear Energy 165, 108772 (2022) [Google Scholar]
- Szames, E., Ammar, K., Tomatis, D., Martinez, J.M., EPJ Web Conf. 247, 06029 (2021) [CrossRef] [EDP Sciences] [Google Scholar]
- V.L. Nicolas Martin, Zachary Prince, M. Tano-Retamales, Nuclear Science and Engineering 197, 1406 (2023) [CrossRef] [Google Scholar]
- Y.M. Chan, J. Dufek, Annals of Nuclear Energy 195, 110123 (2024) [Google Scholar]
- D. Tomatis, EPJ Nuclear Sciences & Technologies (2021) [Google Scholar]
- E. Szames, Ph.D. thesis, University of Paris-Saclay (2020) [Google Scholar]
- O. Truffinet, K. Ammar, N. Gérard Castaing, J.P. Argaud, B. Bouriquet, Annals of Nuclear Energy 185, 109705 (2023) [Google Scholar]
- C.E. Rasmussen, C.K.I. Williams, Gaussian processes for machine learning, Adaptive computation and machine learning (MIT Press, 2006) [Google Scholar]
- M.A. Alvarez, L. Rosasco, N.D. Lawrence, Foundations and Trends in Machine Learn ing 4, 195 (2012) [CrossRef] [Google Scholar]
- E.V. Bonilla, K. Chai, C. Williams, Multitask Gaussian Process Prediction, in Advances in Neural Information Processing Systems, edited by J. Platt, D. Koller, Y. Singer, S. Roweis (Curran Associates, Inc., 2007), Vol. 20 [Google Scholar]
- M. van der Wilk, V. Dutordoir, S. John, A. Artemev, V. Adam, J. Hensman, A framework for interdomain and multioutput gaussian processes (2020), 2003.01115 [Google Scholar]
- Y. Li, S. Gao, H. Wu, L. Cao, W. Shen, Progress in Nuclear Energy 88, 104 (2016) [CrossRef] [Google Scholar]
- N. Dige, U. Diwekar, Computers & Chemical Engineering 115, 431 (2018) [CrossRef] [Google Scholar]
- D. Ginsbourger, Wiley StatsRef: Statistics Reference Online (2018) [Google Scholar]
- A. Godfrey, Tech. rep., Consortium for Advanced Simulation of LWRs, Oak Ridge National Laboratory (2014) [Google Scholar]
- P. Mosca, L. Bourhrara, A. Calloo, A. Gammicchia, F. Goubioud, L.L. Mao, F. Madiot, F. Malouch, E. Masiello, F. Moreau et al., APOLLO3®: Overview of the new code capabilities for reactor physics analysis, in M&C 2023 (2023) [Google Scholar]
- D.P. Kingma, J. Ba, Adam: A method for stochastic optimization (2017), 1412.6980 [Google Scholar]
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, in Advances in Neural Information Processing Systems (Curran Associates, Inc., 2019), Vol. 32 [Google Scholar]
- J.R. Gardner, G. Pleiss, D. Bindel, K.Q. Weinberger, A.G. Wilson, GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration, in Advances in Neural Information Processing Systems (2018) [Google Scholar]
- H. Liu, J. Cai, Y.S. Ong, Knowledge-Based Systems 144, 102 (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.