Issue
EPJ Nuclear Sci. Technol.
Volume 11, 2025
Euratom Research and Training in 2025: ‘Challenges, achievements and future perspectives’, edited by Roger Garbil, Seif Ben Hadj Hassine, Patrick Blaise, and Christophe Girold
Article Number 36
Number of page(s) 13
DOI https://doi.org/10.1051/epjn/2025029
Published online 22 July 2025
  1. Meeting Climate Change Targets: The Role of Nuclear Energy (NEA, OECD Publishing, Paris, 2022) [Google Scholar]
  2. Energynews, European Nuclear Energy Alliance Calls for Support from the EU Commission, 21 October 2024 [Google Scholar]
  3. L. Malerba, P. Agostini, M. Bertolus, F. Delage, A. Gallais-During, C. Grisolia, K. Liger, P.-F. Giroux, Advances on GenIV structural and fuel materials and cross-cutting activities between fission and fusion, EPJ Nucl. Sci. Technol. 6, 32 (2020) [Google Scholar]
  4. R. Garbil, S. Ben Hadj Hassine, P. Blaise and C. Ferry, Euratom Research and Training in 2022: challenges, achievements and future perspectives, EPJ Nuclear Sci. Technol. 10, E1 (2024) [Google Scholar]
  5. K. Mikityuk, L. Ammirabile, M. Forni, J. Jagielski, N. Girault, A. Horvath, J.-L. Kloosterman, M. Tarantino, A. Vasile, Review of Euratom projects on design, safety assessment, R&D and licensing for ESNII/Gen-IV fast neutron systems, EPJ Nuclear Sci. Technol. 6, 36 (2020) [Google Scholar]
  6. L. Malerba, P. Agostini, M. Angiolini and M. Bertolus, Towards a single European strategic research and innovation agenda on materials for all reactor generations through dedicated projects, EPJ Nuclear Sci. Technol. 8, 36 (2022) [Google Scholar]
  7. L. Malerba, A. Al Mazouzi, M. Bertolus, M. Cologna, P. Efsing, A. Jianu, P. Kinnunen, K.-F. Nilsson, M. Rabung, M. Tarantino, Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations, Energies 15, 1845 (2022) [CrossRef] [Google Scholar]
  8. L. Malerba, M. Bertolus, P. Efsing, P. Kinnunen, A. Al Mazouzi, M. Cologna, K.-F. Nilsson, M. Tarantino, M. Rabung, B. Tanguy, M. Ferreira, Strategic Research Agenda/Final Version, ORIENT-NM Deliverable D2.6 (2022) [Google Scholar]
  9. https://ammt.anl.gov/ [Google Scholar]
  10. S.P. Stier, C. Kreisbeck, H. Ihssen, M.A. Popp, J. Hauch, K. Malek, M. Reynaud, T. Goumans, J. Carlsson, I. Todorov, L. Gold, A. Räder, W. Wenzel, S. T. Bandesha, P. Jacques, F. Garcia-Moreno, O. Arcelus, P. Friederich, S. Clark, M. Maglione, A. Laukkanen, I. E. Castelli, J. Carrasco, M. C. Cabanas, H. S. Stein, O. Ozcan, D. Elbert, K. Reuter, C. Scheurer, M. Demura, S. S. Han, T. Vegge, S. Nakamae, M. Fabrizio, M. Kozdras, Adv. Mater. 36, 2407791 (2024) [Google Scholar]
  11. https://www.big-map.eu/ [Google Scholar]
  12. M. Vogler, J. Busk, H. Hajiyani, P.B. Jørgensen, N. Safaei, I.E. Castelli, F.F. Ramirez, J. Carlsson, G. Pizzi, S. Clark, F. Hanke, A. Bhowmik, H.S. Stein, Matter 6, 2647 (2023) [Google Scholar]
  13. B.A. Wilson, A. Conant, T.L. Ulrich, A. Kercher, L.R. Sadergaski, T. Gerczak, A.T. Nelson, C.M. Petrie, J. Harp, A.E. Shields, Nuclear fuel irradiation testbed for nuclear security applications, Front. Nucl. Eng., Sec. Nucl. Mater 2, 1123134 (2023) [Google Scholar]
  14. J. Gao, L. Ma, C. Qing, T. Zhao, Z. Wang, J. Geng, Y. Li, A Health Monitoring Model for Circulation Water Pumps in a Nuclear Power Plant Based on Graph Neural Network Observer, Sensors 24, 4486 (2024) [Google Scholar]
  15. State-of-the-Art Report on Multi scale Modelling Methods (NEA, OECD Publishing, Paris, 2020) [Google Scholar]
  16. S.L. Brunton, J.N. Kutz, Methods for data-driven multiscale model discovery for materials, J. Phys. Mater. 2, 044002 (2019) [Google Scholar]
  17. S. Riva, C. Introini, A. Cammi, Multi-physics model bias correction with data-driven reduced order techniques: Application to nuclear case studies, Appl. Math. Model. 135, 243 (2024) [Google Scholar]
  18. B. Hjørland, Knowledge organization, Knowl. Organ. 43, 475 (2016) [Google Scholar]
  19. J. Friis, G. Goldbeck, S. Gouttebroze, F. Lønstad Bleken, E. Ghedini, Materials Science and Ontologies, in Digitalization and Sustainable Manufacturing: Twin Transition in Norway edited by S. Gulbrandsen-Dahl, H.C. Dreyer, E.L. Hinrichsen, H. Holtskog, K. Martinsen, H. Raabe, G. Sziebig, 1st edn. (Routledge, 2024) [Google Scholar]
  20. A. De Baas, P. Del Nostro, J. Friis, E. Ghedini, G. Goldbeck, I.M. Paponetti, A. Pozzi, A. Sarkar, L. Yang, F.A. Zaccarini, D. Toti, Review and Alignment of Domain-Level Ontologies for Materials Science, IEEE Access, 11, 120372 (2023) [Google Scholar]
  21. https://nextgen.dome40.io/about [Google Scholar]
  22. M.D. Wilkinson et al., The FAIR Guiding Principles for Scientific Data Management and Stewardship, Sci. Data 3, 160018 (2016) [Google Scholar]
  23. Exploring Semantic Technologies and Their Application to Nuclear Knowledge Management, IAEA Nuclear Energy Series No. NG-T-6.15 (IAEA, Vienna, 2021) [Google Scholar]
  24. J. Arenas, M.S. Pérez, M. Serrano, L. Malerba, H. Hein, Specification of ENTENTE database, D2.2 of the H2020 ENTENTE project, (2022) [Google Scholar]
  25. https://entente.linkeddata.es [Google Scholar]
  26. H. Over, E. Wolfart, W. Dietz, L. Toth, Adv. Eng. Mater. 7, 766 (2005) [Google Scholar]
  27. S. Taller, G. VanCoevering, B.D. Wirth, et al., Predicting structural material degradation in advanced nuclear reactors with ion irradiation, Sci. Rep. 11, 2949 (2021) [Google Scholar]
  28. G.S. Was, Challenges to the use of ion irradiation for emulating reactor irradiation. J. Mater. Res. 30, 1158 (2015) [Google Scholar]
  29. M.M. Flores-Leonar, L.M. Mejía-Mendoza, A. Aguilar-Granda, B. Sanchez-Lengeling, H. Tribukait, C. Amador-Bedolla, A. Aspuru-Guzik, Materials Acceleration Platforms: On the way to autonomous experimentation, Curr. Opin. Green Sustain. Chem. 25, 100370 (2020) [Google Scholar]
  30. K.A. Terrani, N.A. Capps, M.J. Kerr, C.A. Back, A.T. Nelson, B.D. Wirth, S.L. Hayes, C.R. Stanek, Accelerating nuclear fuel development and qualification: Modeling and simulation integrated with separate-effects testing, J. Nucl. Mater. 539, 152267 (2020) [Google Scholar]
  31. J.A. Aguiar, A.M. Jokisaari, M. Kerret al., Bringing nuclear materials discovery and qualification into the 21st century, Nat. Commun. 11, 2556 (2020) [Google Scholar]
  32. G.E. Lucas, Review of small specimen test techniques for irradiation testing, Metall. Trans. A 21, 1105 (1990) [Google Scholar]
  33. A. Prasitthipayong, D. Frazer, A. Kareer, M.D. Abad, A. Garner, B. Joni, T. Ungar, G. Ribarik, M. Preuss, L. Balogh, S.J. Tumey, A.M. Minor, P. Hosemann, Micro mechanical testing of candidate structural alloys for Gen-IV nuclear reactors, Nucl. Mater. Energy 16, 34 (2018) [Google Scholar]
  34. M. Rabung, M. Kopp, A. Gasparics, G. Vértesy, I. Szenthe, I. Uytdenhouwen, K. Szielasko, Micromagnetic Characterization of Operation-Induced Damage in Charpy Specimens of RPV Steels, Appl. Sci. 11, 2917 (2021) [Google Scholar]
  35. B. Valeske, A. Osman, F. Römer, R. Tschuncky, Next Generation NDE Sensor Systems as IIoT Elements of Industry 4.0, Res. Nondestruct. Eval. 31, 340 (2020) [Google Scholar]
  36. K. Mangalampalli, P. Ghosh, F. Volpi, D. Kiener, A. Useinov; Advances in multi-scale mechanical characterization. J. Appl. Phys. 132, 220401 (2022) [Google Scholar]
  37. D. Morgan, G. Pilania, A. Couet, B.P. Uberuaga, C. Sun, J. Li, Machine learning in nuclear materials research, Curr. Opin. Solid State Mater. Sci. 26, 100975 (2022) [Google Scholar]
  38. N. Kovachki, B. Liu, X. Sun, H. Zhou, K. Bhattacharya, M. Ortiz, A. Stuart, Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization, Mech. Mater. 165, 104156 (2022) [Google Scholar]
  39. K. Karapiperis, L. Stainier, M. Ortiz, J.E. Andrade, Data-Driven multiscale modeling in mechanics, J. Mech. Phys. Solids 147, 104239 (2021) [Google Scholar]
  40. Y. Wang, Q. Yao, J.T. Kwok, L.M. Ni, Generalizing from a Few Examples: A Survey on Few-shot Learning. ACM Comput. Surv. 53, 1 (2020) [Google Scholar]
  41. https://snetp.eu/offerr/ [Google Scholar]
  42. https://www.oecd-nea.org/jcms/pl_70867/second-framework-for-irradiation-experiments-fides-ii [Google Scholar]

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