Open Access
Issue
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
Article Number 17003
Number of page(s) 10
Section Artificial Intelligence & Digital in Nuclear Applications - Quantum Computing
DOI https://doi.org/10.1051/epjconf/202430217003
Published online 15 October 2024
  1. T. Lassila, A. Manzoni, A. Quarteroni, G. Rozza, Model Order Reduction in Fluid Dynamics: Challenges and Perspectives (Springer International Publishing, Cham, 2014), pp. 235–273, ISBN 978-3-319-02090-7 [Google Scholar]
  2. S.L. Brunton, N.J. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2nd edn. (Cambridge University Press, USA, 2022), ISBN 1-00-909848-9 [CrossRef] [Google Scholar]
  3. S. Riva, C. Introini, A. Cammi, Multi-physics model bias correction with data-driven reduced order modelling techniques: Application to nuclear case studies (2024), 2401.07300 [Google Scholar]
  4. N. Baker, F. Alexander, T. Bremer, A. Hagberg, Y. Kevrekidis, H. Najm, M. Parashar, A. Patra, J. Sethian, S. Wild et al., Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence (2019) [CrossRef] [Google Scholar]
  5. Y. Maday, A. Patera, J. Penn, M. Yano, International Journal for Numerical Methods in Engineering 102 (2014) [Google Scholar]
  6. Y. Maday, O. Mula, Springer INdAM Series pp. 221–235 (2013) [CrossRef] [Google Scholar]
  7. C. Introini, S. Cavalleri, S. Lorenzi, S. Riva, A. Cammi, Computer Methods in Applied Mechanics and Engineering 404, 115773 (2023) [CrossRef] [Google Scholar]
  8. C. Introini, S. Riva, S. Lorenzi, S. Cavalleri, A. Cammi, Annals of Nuclear Energy 182, 109538 (2023) [Google Scholar]
  9. A. Cammi, S. Riva, C. Introini, L. Loi, E. Padovani, Nuclear Engineering and Design 421, 113105 (2024) [CrossRef] [Google Scholar]
  10. F. Cannarile, P. Baraldi, P. Colombo, E. Zio, International Journal of Prognostics and Health Management 9 (2018), number: 1 [Google Scholar]
  11. C.E. Rasmussen, C.K.I. Williams, Gaussian processes for machine learning., Adaptive computation and machine learning (MIT Press, 2006), ISBN 0-262-18253-X [Google Scholar]
  12. M. Brovchenko, E. Merle Lucotte, H. Rouch, F. Alcaro, M. Allibert, M. Aufiero, A. Cammi, S. Dulla, O. Feynberg, L. Frima et al., Optimization of the pre-conceptual design of the MSFR (2013) [Google Scholar]
  13. A. Carrassi, M. Bocquet, L. Bertino, G. Evensen, WIREs Climate Change 9, e535 (2018) [CrossRef] [Google Scholar]
  14. S. Riva, S. Deanesi, C. Introini, S. Lorenzi, A. Cammi, Neutron Flux Reconstruction from Out-Core Sparse Measurements using Data-Driven Reduced Order Modelling, in International Conference on Physics of Reactors (PHYSOR24) (San Francisco, USA, 2024), accepted for presentation [Google Scholar]
  15. M. Aufiero, Ph.D. thesis, Politecnico di Milano (2014) [Google Scholar]

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