| Title: |
Summary of the 5th IAEA technical meeting on fusion data processing, validation and analysis (FDPVA) |
| Authors: |
Xu, M.; Mazon, D.; Barbarino, M.; Biel, Wolfgang; Churchill, R. M.; Fischer, R.; Fujii, K.; Jain, P.; Murari, A.; Pinches, S. D.; Rodriguez-Fernandez, P.; Stillerman, J.; Vega, J.; Verdoolaege, Geert; Yokoyama, M.; Abreu, P.; Ahmed, S.; Alhage, Jerome; Almuhisen, F.; Bergmann, M.; Pereira Botelho, D.; Caputo, Leonardo; Carli, S.; Castro, R.; Craciunescu, T.; Deeba, F.; Esquembre, F.; Giil, K.; Gu, Y.; Hall, Joseph; Hollocombe, J.; Huang, X.; Jardin, A.; Jorge, R.; Li, Y.; Liu, Y.; Mcintosh, S.; Peluso, E.; Rossi, R.; Ruiz, M.; De Rycke, Jeffrey; Schneider, M.; Sertoli, M.; Puig Sitjes, A.; Stieglitz, D.; Tan, Y.; Weisen, H.; Wu, Hao; Wyss, I.; Zang, L. |
| Source: |
NUCLEAR FUSION ; ISSN: 0029-5515 ; ISSN: 1741-4326 |
| Publication Year: |
2026 |
| Collection: |
Ghent University Academic Bibliography |
| Subject Terms: |
Technology and Engineering; plasma diagnostics; fusion databases; integrated data analysis; machine learning; JET NEUTRON; TRANSPORT; DESIGN; MODEL |
| Description: |
The purpose of the 5th International Atomic Energy Agency technical meeting on fusion data processing, validation and analysis (FDPVA) (Ghent University, Ghent, Belgium, 12-15 June 2023) was to provide a platform during which a set of topics relevant to FDPVA were discussed with the view of meeting the needs of next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics data analysis and synthetic diagnostics reported at the meeting, including concept description of new devices; fusion databases; integrated data analysis; inverse problems; uncertainty propagation, verification and validation; probabilistic methods and machine learning. The relevant results underline trends observed in the current major fusion confinement devices. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://biblio.ugent.be/publication/01KM7YCG5516PWAYY2NVFSHM1Z; https://biblio.ugent.be/publication/01KM7YCG5516PWAYY2NVFSHM1Z/file/01KMN9DQF3BTT663PQ4981HW66 |
| DOI: |
10.1088/1741-4326/ae048d |
| Availability: |
https://biblio.ugent.be/publication/01KM7YCG5516PWAYY2NVFSHM1Z; https://hdl.handle.net/1854/LU-01KM7YCG5516PWAYY2NVFSHM1Z; https://doi.org/10.1088/1741-4326/ae048d; https://biblio.ugent.be/publication/01KM7YCG5516PWAYY2NVFSHM1Z/file/01KMN9DQF3BTT663PQ4981HW66 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.5E53B676 |
| Database: |
BASE |