State representation learning algorithms for data-driven predictions of tokamak pedestals

A. E. Järvinen, Adam Kit, Amanda Bruncrona, Y. Poels, S. Wiesen, V. Menkovski, L. Frassinetti, M. Dunne, S. Saarelma

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Scientific

Original languageEnglish
Title of host publication50th EPS Conference on Plasma Physics (EPS 2024)
PublisherEPS Publishing
Volume48A
ISBN (Electronic)9798331305239
Publication statusPublished - 2024
MoE publication typeNot Eligible
Event50th EPS Conference on Plasma Physics, EPS 2024 - Salamanca, Spain
Duration: 8 Jul 2024 → 12 Jul 2024
https://lac913.epfl.ch/epsppd3/2024/html/index.html

Publication series

SeriesEurophysics Conference Abstracts
Volume48A
ISSN0378-2271

Conference

Conference50th EPS Conference on Plasma Physics, EPS 2024
Country/TerritorySpain
CitySalamanca
Period8/07/24 → 12/07/24
Internet address

Funding

This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No. 101052200\u2014EUROfusion). The work of Aaro Järvinen, Adam Kit, and Amanda Bruncrona was partially supported by the Research Council of Finland Grant No. 355460. The authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. The relevant CSC Project Numbers are 2005083 and 2009007.

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