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@INPROCEEDINGS{PunnoliValayanad:1033885,
      author       = {Punnoli Valayanad, Swathikrishna and Arnold, Lukas},
      title        = {{AI}-{B}ased {M}aterial {P}arameter {P}rediction from
                      {C}one {C}alorimeter {M}easurements},
      journal      = {Journal of physics / Conference Series},
      volume       = {2885},
      number       = {1},
      issn         = {1742-6588},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {FZJ-2024-06723},
      pages        = {012013 -},
      year         = {2024},
      abstract     = {In pyrolysis simulation, methods such as inverse modelling
                      are used to determine the material parameters from
                      small-scale experiments. This often requires significant
                      computational resources due to its iterative nature. This
                      study investigates an artificial intelligence (AI)based
                      alternative approach, that can give instantaneous
                      predictions for material parameters once trained. A dataset
                      based on Fire Dynamics Simulator (FDS) simulation of a cone
                      calorimeter experiment is used for training these AI models.
                      Di!erent AI models are trained to predict polymethyl
                      methacrylate’s thermo-physical parameters using heat
                      release rate (HRR) curves as input. AI models including
                      Random Forest, 1D-convolution, and Recurrent Neural Networks
                      showed the ability to predict the material parameters
                      accurately with low mean squared error on the test dataset.
                      These models were also able to recreate the HRR curves in
                      FDS using their predictions, following the trend of the
                      experimental HRR curve closely. Expanding the dataset to
                      include materials with di!erent behaviours and modelling
                      di!erent experiments could give these AI models broader
                      applicability. However, FDS version dependence is a
                      limitation for the AI models explored here because they were
                      trained on a simulation-based dataset. Looking at the
                      results, AI models in general can be used to predict
                      material parameters required for pyrolysis modelling,
                      potentially saving time and e!ort or, at the very least,
                      used to complement the existing inverse modelling
                      approaches.},
      month         = {Oct},
      date          = {2024-10-09},
      organization  = {4th European Symposium on Fire Safety
                       Science, Barcelona (Spain), 9 Oct 2024
                       - 11 Oct 2024},
      cin          = {IAS-7},
      ddc          = {530},
      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 / PUB:(DE-HGF)8},
      UT           = {WOS:001407642900013},
      doi          = {10.1088/1742-6596/2885/1/012013},
      url          = {https://juser.fz-juelich.de/record/1033885},
}