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@ARTICLE{Holthusen:1005558,
      author       = {Holthusen, Hagen and Brepols, Tim and Linka, Kevin and
                      Kuhl, Ellen},
      title        = {{A}utomated model discovery for tensional homeostasis:
                      {C}onstitutive machine learning in growth and remodeling},
      journal      = {Computers in biology and medicine},
      volume       = {186},
      issn         = {1879-0534},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {RWTH-2025-01843},
      pages        = {109691},
      year         = {2025},
      cin          = {311510},
      ddc          = {570},
      cid          = {$I:(DE-82)311510_20140620$},
      pnm          = {OAPKF - Open-Access-Publikation mit Unterstützung der RWTH
                      Aachen University (021000-OAPKF) / DFG project
                      G:(GEPRIS)417002380 - TRR 280: Konstruktionsstrategien für
                      materialminimierte Carbonbetonstrukturen – Grundlagen für
                      eine neue Art zu bauen (417002380) / DFG project
                      G:(GEPRIS)453596084 - TRR 339: Digitaler Zwilling Straße
                      – Physikalisch-informatorische Abbildung des Systems
                      „Straße der Zukunft“ (453596084) / DFG project
                      G:(GEPRIS)533187597 - Computational Intelligence für die
                      Mechanik weicher Materialien (533187597) / DFG project
                      G:(GEPRIS)465213526 - In-Stent-Restenose in koronaren
                      Arterien - In silico-Untersuchungen basierend auf
                      patientenspezifischen Daten und Metamodellierung (465213526)
                      / DISCOVER - Automated Model Discovery for Soft Matter
                      Systems (101141626)},
      pid          = {G:(DE-82)021000-OAPKF / G:(GEPRIS)417002380 /
                      G:(GEPRIS)453596084 / G:(GEPRIS)533187597 /
                      G:(GEPRIS)465213526 / G:(EU-Grant)101141626},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.compbiomed.2025.109691},
      url          = {https://publications.rwth-aachen.de/record/1005558},
}