Supervised learning approaches to modeling pedestal density

Adam Kit (Corresponding Author), Aaro Järvinen, L. Frassinetti, S. Wiesen, JET Contributors

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number045003
Number of pages12
JournalPlasma Physics and Controlled Fusion
Volume65
Issue number4
DOIs
Publication statusPublished - Apr 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • fusion
  • machine learning
  • pedestal

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