Scheduled presentations:

• Physics-augmented neural networks for hyperelastic material modeling with softening
  *J. Fey (TU Darmstadt), D. Klein (TU Darmstadt), O. Weeger (TU Darmstadt)
• Addressing Compressibility in Hyperelastic Materials: A Comparative Study of Classical Constitutive Laws and Physics-Augmented Neural Networks
  *L. Maurer (Otto von Guericke University Magdeburg), S. Eisenträger (Otto von Guericke University Magdeburg), D. Juhre (Otto von Guericke University Magdeburg)
• Unsupervised Learning of Constitutive Model With Neural Networks and Sparse Identification of Internal Variables
  *A. Benady (ETH Zurich), B. Emmanuel (Laboratoire mécanique Paris-Saclay), C. Jailin (Laboratoire mécanique Paris-Saclay), L. Chamoin (Laboratoire mécanique Paris-Saclay)
• Data-driven modeling based on the Constitutive Relation Error for history-dependent materials
  *P. LADEVEZE (ENS Paris-Saclay), L. CHAMOIN (ENS Paris-Saclay)
• Differentiable Constitutive Modeling in a Machine Learning-Compatible Framework
  *J. Bleyer (Ecole nationale des ponts et chaussées)