Machine learning surrogate model for pedestal MHD stability

Amanda Bruncrona; Adam Kit; Aaro Järvinen; Samuli Saarelma; Lorenzo Frassinetti; Hampus Nyström; Arnaud Lafay-Labrosse; JET Contributors

Machine learning surrogate model for pedestal MHD stability

Amanda Bruncrona
, Adam Kit
, Aaro Järvinen
, Samuli Saarelma
, Lorenzo Frassinetti
, Hampus Nyström
, Arnaud Lafay-Labrosse
, JET Contributors

BA4511 Fusion energy

University of Helsinki
National Nuclear Laboratory (Abingdon)
KTH Royal Institute of Technology

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

Original language	English
Title of host publication	51st EPS Conference on Plasma Physics
Publisher	European Physical Society
Publication status	Published - 2025
MoE publication type	Not Eligible
Event	51st EPS Conference on Plasma Physics, EPS 2025 - Vilnius, Lithuania Duration: 7 Jul 2025 → 11 Jul 2025

Publication series

Series	Europhysics Conference Abstracts
Volume	51A
ISSN	0378-2271

Conference

Conference	51st EPS Conference on Plasma Physics, EPS 2025
Country/Territory	Lithuania
City	Vilnius
Period	7/07/25 → 11/07/25

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 — EUROfusion).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

plasma
MHD
machine learning
magnetohydrodynamic stability
magnetohydrodynamics
magnetohydrodynamic simulation
surrogate model
fusion energy

Access to Document

https://lac913.epfl.ch/epsppd3/2025/html/PDF/P2_132.pdf

Cite this

@inbook{1fdb01791c4646548a0919ca7356398b,

title = "Machine learning surrogate model for pedestal MHD stability",

keywords = "plasma, MHD, machine learning, magnetohydrodynamic stability, magnetohydrodynamics, magnetohydrodynamic simulation, surrogate model, fusion energy",

author = "Amanda Bruncrona and Adam Kit and Aaro J{\"a}rvinen and Samuli Saarelma and Lorenzo Frassinetti and Hampus Nystr{\"o}m and Arnaud Lafay-Labrosse and \{JET Contributors\}",

note = "Failit pit{\"a}isi olla lehtiartikkelissa, eli t{\"a}m{\"a} entry on vain ECA; 51st EPS Conference on Plasma Physics, EPS 2025 ; Conference date: 07-07-2025 Through 11-07-2025",

year = "2025",

language = "English",

series = "Europhysics Conference Abstracts",

publisher = "European Physical Society",

booktitle = "51st EPS Conference on Plasma Physics",

address = "France",

}

Bruncrona, A , Kit, A , Järvinen, A, Saarelma, S, Frassinetti, L, Nyström, H, Lafay-Labrosse, A & JET Contributors 2025, Machine learning surrogate model for pedestal MHD stability. in 51st EPS Conference on Plasma Physics. European Physical Society, Europhysics Conference Abstracts, vol. 51A, 51st EPS Conference on Plasma Physics, EPS 2025, Vilnius, Lithuania, 7/07/25. <https://lac913.epfl.ch/epsppd3/2025/html/PDF/P2_132.pdf>

TY - CHAP

T1 - Machine learning surrogate model for pedestal MHD stability

AU - Bruncrona, Amanda

AU - Kit, Adam

AU - Järvinen, Aaro

AU - Saarelma, Samuli

AU - Frassinetti, Lorenzo

AU - Nyström, Hampus

AU - Lafay-Labrosse, Arnaud

AU - JET Contributors

N1 - Failit pitäisi olla lehtiartikkelissa, eli tämä entry on vain ECA

PY - 2025

Y1 - 2025

KW - plasma

KW - MHD

KW - machine learning

KW - magnetohydrodynamic stability

KW - magnetohydrodynamics

KW - magnetohydrodynamic simulation

KW - surrogate model

KW - fusion energy

M3 - Conference abstract in proceedings

T3 - Europhysics Conference Abstracts

BT - 51st EPS Conference on Plasma Physics

PB - European Physical Society

T2 - 51st EPS Conference on Plasma Physics, EPS 2025

Y2 - 7 July 2025 through 11 July 2025

ER -

Machine learning surrogate model for pedestal MHD stability

Publication series

Conference

Funding

UN SDGs

Keywords

Access to Document

Other files and links

DEEPfusion: Artificial Intelligence for Fusion Reactor Predictions

Cite this

Machine learning surrogate model for pedestal MHD stability

Publication series

Conference

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Projects

DEEPfusion: Artificial Intelligence for Fusion Reactor Predictions

Cite this