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@ARTICLE{Dasbach:1006426,
      author       = {Dasbach, Stefan and Wiesen, Sven},
      title        = {{T}owards fast surrogate models for interpolation of
                      tokamak edge plasmas},
      journal      = {Nuclear materials and energy},
      volume       = {34},
      issn         = {2352-1791},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2023-01661},
      pages        = {101396 -},
      year         = {2023},
      abstract     = {One of the major design limitations for tokamak fusion
                      reactors is the heat load that can be sustained by the
                      materials at the divertor target. Developing a full
                      understanding of how machine or operation parameters affect
                      the conditions at the divertor requires an enormous number
                      of simulations. A promising approach to circumvent this is
                      to use machine learning models trained on simulation data as
                      surrogate models. Once trained such surrogate models can
                      make fast predictions for any scenario in the design
                      parameter space. In future such simulation based surrogate
                      models could be used in system codes for rapid design
                      studies of future fusion power plants. This work presents
                      the first steps towards the development of such surrogate
                      models for plasma exhaust and the datasets required for
                      their training. Machine learning models like neural networks
                      usually require several thousand data points for training,
                      but the exact amount of data required varies from case to
                      case. Due to the long runtimes of simulations we aim at
                      finding the minimal amount of training data required. A
                      preliminary dataset based on SOLPS-ITER simulations with
                      varying tokamak design parameters, including the major
                      radius, magnetic field strength and neutral density is
                      constructed. To be able to generate more training data
                      within reasonable computation time the simulations in the
                      dataset use fluid neutral simulations and no fluid drift
                      effects. The dataset is used to train a simple neural
                      network and Gradient Boosted Regression Trees and test how
                      the performance depends on the number of training
                      simulations.},
      cin          = {IEK-4},
      ddc          = {624},
      cid          = {I:(DE-Juel1)IEK-4-20101013},
      pnm          = {134 - Plasma-Wand-Wechselwirkung (POF4-134)},
      pid          = {G:(DE-HGF)POF4-134},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000990102500001},
      doi          = {10.1016/j.nme.2023.101396},
      url          = {https://juser.fz-juelich.de/record/1006426},
}