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@ARTICLE{Lauer:972115,
      author       = {Lauer, Patrick and Arnold, Lukas and Brännström, Fabian},
      title        = {{I}nverse modelling of pyrolization kinetics with ensemble
                      learning methods},
      journal      = {Fire safety journal},
      volume       = {136},
      issn         = {0378-7761},
      address      = {New York, NY [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2023-01077},
      pages        = {103744 -},
      year         = {2023},
      abstract     = {To simulate fire spread, especially the pyrolysis process
                      – the thermal decomposition of a solid material – must
                      be predicted. Yet, needed material dependent reaction
                      kinetic parameters cannot be directly measured. Common
                      methods infer them from small scale tests with inverse
                      modelling approaches, which are computationally costly.
                      Here, a novel machine learning based approach utilising
                      extremely randomized trees (ERT) is presented and evaluated.
                      It aims to derive these parameters almost instantly due to
                      an inverse pre-trained surrogate model. The approach
                      consists of an ERT classifier, a non-linear least squares
                      optimiser and an ERT regressor. A thorough hyperparameter
                      study was conducted. The model is evaluated with a synthetic
                      thermogravimetric analysis (TGA) dataset. Calculated from an
                      Arrhenius model, it contains data for more than synthetic
                      materials consisting of up to three components. The method
                      is also applied on real experimental data, here polymethyl
                      methacrylate (PMMA), gained from the Measurement and
                      Computation of Fire Phenomena (MacFP) working group.
                      Evaluation of the model demonstrated that it can instantly
                      predict reaction kinetic parameters from TGA experiments for
                      synthetic and real materials. Systematic analysis showed an
                      overall score of 0.77 for the complete model predictions.
                      The code and datasets are published as open access.},
      cin          = {IAS-7},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000927547500001},
      doi          = {10.1016/j.firesaf.2023.103744},
      url          = {https://juser.fz-juelich.de/record/972115},
}