Scheduled presentations:

• Automating constitutive modeling with LLMs
  *M. Tacke (Helmholtz-Zentrum hereon), M. Busch (Hamburg University of Technology), K. Abdolazizi (Hamburg University of Technology), J. Eichinger (Helmholtz-Zentrum hereon), K. Linka (Hamburg University of Technology), C. Cyron (Helmholtz-Zentrum hereon), R. Aydin (Helmholtz-Zentrum hereon)
• Hetero-EUCLID: Interpretable Model Discovery for Heterogeneous Hyperelastic Materials Using Stress-Unsupervised Learning
  *K. Chaurasiya (Indian Institute of Science, Bengaluru), S. Dutta (Indian Institute of Science, Bengaluru), S. Kumar (Delft University of Technology), A. Joshi (Indian Institute of Science, Bengaluru)
• Material Fingerprinting for ultra-fast material model discovery without solving optimization problems
  *M. Flaschel (FAU Erlangen), M. Denisa (FAU Erlangen), M. Moreno-Mateos (FAU Erlangen), S. Wiesheier (FAU Erlangen), C. Veil (Stanford University), P. Steinmann (FAU Erlangen), E. Kuhl (Stanford University)
• Inelastic Constitutive Kolmogorov-Arnold Networks: A Generalized Framework for Automated Discovery of Interpretable Inelastic Material Models
  *C. Ji (CoMed, AME, RWTH Aachen University), K. Abdolazizi (ICM, Hamburg University of Technology), H. Holthusen (LTM, University of Erlangen-Nuremberg), C. Cyron (ICM, Hamburg University of Technology), K. Linka (CoMed, AME, RWTH Aachen University)
• Constitutive Kolmogorov–Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling
  *K. Abdolazizi (Hamburg University of Technology), C. Cyron (Hamburg University of Technology), K. Linka (RWTH Aachen University)
• Data-Rich or Data-Right? Investigating Training Data Requirements for Constitutive Artificial Neural Networks
  *M. Knipper (RWTH Aachen), M. Brand (RWTH Aachen), K. Linka (RWTH Aachen)