TY  - JOUR
AU  - Brenzke, M.
AU  - Wiesen, S.
AU  - Bernert, M.
AU  - Coster, D.
AU  - Jitsev, Jenia
AU  - Liang, Yunfeng
AU  - von Toussaint, U.
AU  - ASDEX Upgrade Team
AU  - EUROfusion MST1 Team
TI  - Divertor power load predictions based on machine learning
JO  - Nuclear fusion
VL  - 61
IS  - 4
SN  - 0029-5515
CY  - Vienna
PB  - IAEA
M1  - FZJ-2022-00867
SP  - 046023 -
PY  - 2021
N1  - kein Zugriff auf Postprint
AB  - Machine learning based data-driven approaches to thermal load prediction on the divertor targets of ASDEX upgrade (AUG) are presented. After selecting time averaged data from almost six years of operation of AUG and applying basic physics-motivated cuts to the data we find that we are able to train machine learning models to predict a scalar quantifying the steady state thermal loads on the outer divertor target given scalar operational parameters. With both random forest and neural network based models we manage to achieve decent agreement between the model predictions and the observed values from experiments. Furthermore, we investigate the dependencies of the models and observe that the models manage to extract trends expected from previous physics analyses.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000629939700001
DO  - DOI:10.1088/1741-4326/abdb94
UR  - https://juser.fz-juelich.de/record/905640
ER  -