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@ARTICLE{Mayfrank:1044192,
      author       = {Mayfrank, Daniel and Velioglu, Mehmet and Mitsos, Alexander
                      and Dahmen, Manuel},
      title        = {{S}ample-efficient reinforcement learning of {K}oopman
                      e{NMPC}},
      journal      = {Computers $\&$ chemical engineering},
      volume       = {201},
      issn         = {0098-1354},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2025-03081},
      pages        = {109240 -},
      year         = {2025},
      cin          = {ICE-1},
      ddc          = {660},
      cid          = {I:(DE-Juel1)ICE-1-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112) / HDS LEE - Helmholtz
                      School for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612) / DFG project G:(GEPRIS)466656378 -
                      Dateneffizientes und physikinformiertes Verstärkungslernen
                      zur Regelung von flüssig-flüssig Trennprozessen
                      (466656378)},
      pid          = {G:(DE-HGF)POF4-1121 / G:(DE-Juel1)HDS-LEE-20190612 /
                      G:(GEPRIS)466656378},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001521405800002},
      doi          = {10.1016/j.compchemeng.2025.109240},
      url          = {https://juser.fz-juelich.de/record/1044192},
}