@phdthesis{Boeckenhoff2019,
  author    = {Daniel B{\"o}ckenhoff},
  title     = {En Route Towards Heat Load Control for Wendelstein 7-X with Machine Learning Approaches},
  journal    = {Studien zur W{\"a}rmelastkontrolle f{\"u}r Wendelstein 7-X mit Maschinellem Lernen},
  url       = {https://nbn-resolving.org/urn:nbn:de:gbv:9-opus-35780},
  pages     = {157},
  year      = {2019},
  abstract  = {With this thesis, studies which form the bedrock for the long term goal of first wall heat load control and optimization for the advanced stellarator Wendelstein 7-X are developed, described and put into context. It is laid out how reconstruction of features of the edge magnetic field from plasma facing component heat loads is an important first step and can successfully be achieved by artificial neural networks. A detailed study of plasma facing component heat load distribution, potential overloads and overload mitigation possibilities is made in first order approximation of the impact of the main plasma dynamic effects.},
  language  = {en}
}