Scheduled presentations:

• Isotropic Polyconvex Hyperelastic Energies and Hulls: A Novel Neural Network Framework Satisfying the Universal Approximation Theorem
  *G. Geuken (TU Dortmund University), P. Kurzeja (TU Dortmund University), D. Wiedemann (TU Dortmund University), J. Mosler (TU Dortmund University)
• On the role of interpretability of data-driven constitutive modeling by Constitutive Artificial Neural Networks
  *K. Abdolazizi (Hamburg University of Technology), C. Cyron (Hamburg University of Technology), K. Linka (Hamburg University of Technology)
• Student A Blind Source Separation Perspective on the Model Identification Problem for Constitutive Material Laws
  *L. Mabileau (LMPS - Laboratoire de Mécanique Paris-Saclay), C. Jailin (LMPS - Laboratoire de Mécanique Paris-Saclay), E. Baranger (LMPS - Laboratoire de Mécanique Paris-Saclay)
• Symmetric and Parameterized Physics-Augmented Neural Networks for Hyperelastic Constitutive Modeling in Beam Theory
  *J. Schommartz (Technical University of Darmstadt), D. Klein (Technical University of Darmstadt), J. Alzate Cobo (Technical University of Darmstadt), O. Weeger (Technical University of Darmstadt)
• Neural Networks meet Hyperelasticity: On Benefits and Limits of Polyconvexity
  *D. Klein (TU Darmstadt), O. Weeger (TU Darmstadt)