Issue
EPJ Nuclear Sci. Technol.
Volume 11, 2025
Special Issue on ‘Overview of recent advances in HPC simulation methods for nuclear applications’, edited by Andrea Zoia, Elie Saikali, Cheikh Diop and Cyrille de Saint Jean
Article Number 55
Number of page(s) 17
DOI https://doi.org/10.1051/epjn/2025054
Published online 16 September 2025
  1. T. Lassila, A. Manzoni, A. Quarteroni, G. Rozza, Model Order Reduction in Fluid Dynamics: Challenges and Perspectives (Springer International Publishing, 2014) [Google Scholar]
  2. G. Rozza et al., Model Order Reduction: Volume 2: Snapshot-Based Methods and Algorithms (De Gruyter, 2020). https://doi.org/10.1515/9783110671490 [Google Scholar]
  3. A. Quarteroni, A. Manzoni, F. Negri, Reduced Basis Methods for Partial Differential Equations: An Introduction, 1st edn., (UNITEXT, Springer Cham, 2015) [Google Scholar]
  4. S.L. Brunton, J.N. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (Cambridge University Press, 2022) [Google Scholar]
  5. A. Carrassi, M. Bocquet, L. Bertino, G. Evensen, Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Climate Change 9, e535 (2018). https://doi.org/10.1002/wcc.535 [CrossRef] [Google Scholar]
  6. S. Riva, C. Introini, A. Cammi, Applied Mathematical Modelling Multi-physics model bias correction with data-driven reduced order techniques: Application to nuclear case studies, Appl. Math. Modell. 135, 243 (2024). https://doi.org/10.1016/j.apm.2024.06.040 [Google Scholar]
  7. N. Baker et al., Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence, Tech. rep., USDOE Office of Science (SC), Washington, D.C. (United States), 2019. https://doi.org/10.2172/1478744 [Google Scholar]
  8. Y. Maday, A.T. Patera, J.D. Penn, M. Yano, A parameterized-background data-weak approach to variational data assimilation: formulation, analysis, and application to acoustics, Int. J. Numer. Methods Eng. 102, 933 (2019). https://doi.org/10.1002/nme.4747 [Google Scholar]
  9. Y. Maday, O. Mula, in A Generalized Empirical Interpolation Method: Application of Reduced Basis Techniques to Data Assimilation (Springer, 2013), pp. 221–235 [Google Scholar]
  10. H. Gong, J.P. Argaud, B. Bouriquet, Y. Maday, The empirical interpolation method applied to the neutron diffusion equations with parameter dependence, in Physics of Reactors 2016, PHYSOR 2016: Unifying Theory and Experiments in the 21st Century, 1 (May) (2016), pp. 54–63 [Google Scholar]
  11. J.-P. Argaud, B. Bouriquet, F. de Caso, H. Gong, Y. Maday, O. Mula, Sensor placement in nuclear reactors based on the generalized empirical interpolation method, J. Comput. Phys. 363, 354 (2018). https://doi.org/10.1016/j.jcp.2018.02.050 [Google Scholar]
  12. H. Gong, Data assimilation with reduced basis and noisy measurement: Applications to nuclear reactor cores, Ph.D. thesis, Sorbonne Université, 2018 [Google Scholar]
  13. C. Introini, S. Riva, S. Lorenzi, S. Cavalleri, A. Cammi, Non-intrusive system state reconstruction from indirect measurements: A novel approach based on hybrid data assimilation methods, Ann. Nucl. Energy 182, 109538 (2023). https://doi.org/10.1016/j.anucene.2022.109538 [Google Scholar]
  14. A. Cammi, S. Riva, C. Introini, L. Loi, E. Padovani, Data-driven model order reduction for sensor positioning and indirect reconstruction with noisy data:Application to a circulating fuel reactor, Nucl. Eng. Des. 421, 113105 (2024). https://doi.org/10.1016/j.nucengdes.2024.113105 [Google Scholar]
  15. H. Gong, Z. Chen, Q. Li, Generalized empirical interpolation method with H1 regularization: application to nuclear reactor physics, Front. Energy Res. 9, 4018 (2022). https://doi.org/10.3389/fenrg.2021.804018 [Google Scholar]
  16. C. Introini, S. Cavalleri, S. Lorenzi, S. Riva, A. Cammi, Stabilization of generalized empirical interpolation method (geim) in presence of noise: A novel approach based on tikhonov regularization, Comput. Methods Appl. Mech. Eng. 404, 115773 (2023). https://doi.org/10.1016/j.cma.2022.115773 [Google Scholar]
  17. F. Cannarile, P. Baraldi, P. Colombo, E. Zio, A novel method for sensor data validation based on the analysis of wavelet transform scalograms, Int. J. Progn. Health Manage. 9, (2020). https://doi.org/10.36001/ijphm.2018.v9i1.2670 [Google Scholar]
  18. V. Rao, A. Sandu, M. Ng, E.D. Nino-Ruiz, Robust data assimilation using l1 and huber norms, SIAM J. Sci. Comput. 39, B548 (2017). https://doi.org/10.1137/15M1045910 [Google Scholar]
  19. B. Peherstorfer, K. Willcox, Dynamic data-driven model reduction: adapting reduced models from incomplete data, Adv. Model. Simul. Eng. Sci. 3, 11 (2016). https://doi.org/10.1186/s40323-016-0064-x [Google Scholar]
  20. A. Hossein Abolmasoumi, M. Netto, L. Mili, Robust dynamic mode decomposition, IEEE Access 10, 65473 (2022). https://doi.org/10.1109/ACCESS.2022.3183760 [Google Scholar]
  21. S. Riva, C. Introini, E. Zio, A. Cammi, Impact of malfunctioning sensors on data-driven reduced order modelling: Application to molten salt reactors, EPJ Web Conf. 302, 17003 (2024). https://doi.org/10.1051/epjconf/202430217003 [Google Scholar]
  22. C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning (MIT Press, 2006) [Google Scholar]
  23. T.K. Ho, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell. 20, 832 (1998). https://doi.org/10.1109/34.709601 [Google Scholar]
  24. M. Brovchenko et al., Design-related studies for the preliminary safety assessment of the molten salt fast reactor, Nucl. Sci. Eng. 175, 329 (2013). https://doi.org/10.13182/NSE12-70 [Google Scholar]
  25. W. Haik, Y. Maday, L. Chamoin, A real-time variational data assimilation method with data-driven model enrichment for time-dependent problems, Comput. Methods Appl. Mech. Eng. 405, 115868 (2023). https://doi.org/10.1016/j.cma.2022.115868 [Google Scholar]
  26. Y. Maday, O. Mula, G. Turinici, Convergence analysis of the generalized empirical interpolation method, SIAM J. Numer. Anal. 54, 1713 (2016). https://doi.org/10.1137/140978843 [Google Scholar]
  27. T. Taddei, Model order reduction methods for data assimilation; state estimation and structural health monitoring, Ph.D. thesis, MIT, 2016. https://doi.org/10.13140/RG.2.2.16001.45928 [Google Scholar]
  28. Y. Maday, T. Taddei, Adaptive PBDW approach to state stimation: Noisy observations; user-defined update spaces, SIAM J. Sci. Comput. 41, B669 (2019). https://doi.org/10.1137/18M116544X [Google Scholar]
  29. O.A. Martin, R. Kumar, J. Lao, Bayesian Modeling and Computation in Python (CRC Press, Boca Raton, 2021) [Google Scholar]
  30. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res. 12, 2825 (2011) [MathSciNet] [Google Scholar]
  31. 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 Proceedings of the International Conference on Physics of Reactors (PHYSOR24), 2024, pp. 1632–1641. [Google Scholar]
  32. M. Aufiero, Development of Advanced Simulation Tools for Circulating Fuel Nuclear Reactors, Ph.D. thesis, Politecnico di Milano, 2014. https://doi.org/10.13140/2.1.4455.1044 [Google Scholar]
  33. F. Casenave, A. Ern, T. Lelièvre, Variants of the empirical interpolation method: Symmetric formulation, choice of norms and rectangular extension, Appl. Math. Lett. 56, 23 (2016). https://doi.org/10.1016/j.aml.2015.11.010 [Google Scholar]
  34. S.L. Brunton, J.N. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2nd edn. (Cambridge University Press, USA, 2022) [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.