TY - JOUR
T1 - Supervised learning approaches to modeling pedestal density
AU - Kit, A.
AU - Järvinen, A. E.
AU - Frassinetti, L.
AU - Wiesen, S.
AU - JET Contributors
N1 - See the author list of ‘Overview of JET results for optimising ITER operation’ by J Mailloux et al Nucl. Fusion 62 042026.
Funding Information:
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). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.
Publisher Copyright:
© 2023 University of Helsinki.
PY - 2023/4
Y1 - 2023/4
N2 - Pedestals are the key to conventional high performance plasma scenarios in tokamaks. However, high fidelity simulations of pedestal plasmas are extremely challenging due to the multiple physical processes and scales that are encompassed by tokamak pedestals. The leading paradigm for predicting the pedestal top pressure is encompassed by EPED-like models. However, EPED does not predict the pedestal top density, n e , p e d , but requires it as an input. EUROPED (Saarelma et al 2019 Phys. Plasmas 26 072501) employs simplified models, such as log-linear regression, to constrain n e , p e d with tokamak machine control parameters in an EPED-like model. However, these simplified models for n e , p e d often show disagreements with experimental observations and do not use all of the available numerical and categorical machine control information. In this work it is observed that using the same input parameters, decision tree ensembles and deep learning models improves the predictive quality of n e , p e d by about 23% relative to that obtained with log-linear scaling laws, measured by root mean square error. Including all of the available tokamak machine control parameters, both numerical and categorical, leads to further improvement of about 13%. Finally, predictive quality was tested when including global normalized plasma pressure and effective charge state as inputs, as these parameters are known to impact pedestals. Surprisingly, these parameters lead to only a few percent further improvement of the predictive quality. The corresponding code for this analysis can be found at github.com/fusionby2030/supervised_learning_jetpdb.
AB - Pedestals are the key to conventional high performance plasma scenarios in tokamaks. However, high fidelity simulations of pedestal plasmas are extremely challenging due to the multiple physical processes and scales that are encompassed by tokamak pedestals. The leading paradigm for predicting the pedestal top pressure is encompassed by EPED-like models. However, EPED does not predict the pedestal top density, n e , p e d , but requires it as an input. EUROPED (Saarelma et al 2019 Phys. Plasmas 26 072501) employs simplified models, such as log-linear regression, to constrain n e , p e d with tokamak machine control parameters in an EPED-like model. However, these simplified models for n e , p e d often show disagreements with experimental observations and do not use all of the available numerical and categorical machine control information. In this work it is observed that using the same input parameters, decision tree ensembles and deep learning models improves the predictive quality of n e , p e d by about 23% relative to that obtained with log-linear scaling laws, measured by root mean square error. Including all of the available tokamak machine control parameters, both numerical and categorical, leads to further improvement of about 13%. Finally, predictive quality was tested when including global normalized plasma pressure and effective charge state as inputs, as these parameters are known to impact pedestals. Surprisingly, these parameters lead to only a few percent further improvement of the predictive quality. The corresponding code for this analysis can be found at github.com/fusionby2030/supervised_learning_jetpdb.
KW - fusion
KW - machine learning
KW - pedestal
UR - http://www.scopus.com/inward/record.url?scp=85148950013&partnerID=8YFLogxK
U2 - 10.1088/1361-6587/acb3f7
DO - 10.1088/1361-6587/acb3f7
M3 - Article
AN - SCOPUS:85148950013
SN - 0741-3335
VL - 65
JO - Plasma Physics and Controlled Fusion
JF - Plasma Physics and Controlled Fusion
IS - 4
M1 - 045003
ER -