Scheduled presentations:

• Physics-augmented neural network material models for hyperelasticity with softening
  J. Fey (TU Darmstadt), D. Klein (TU Darmstadt), *O. Weeger (TU Darmstadt)
• Variational Physics Informed Neural Networks formulation for solid mechanics
  *D. Rodopoulos (New York University Abu Dhabi), P. Pantidis (New York University Abu Dhabi), N. Karathanasopoulos (New York University)
• New Insights Into Strain-Induced Rubber Crystallisation and Fracture: A Precise and Efficient Physics-Augmented Neural Network Approach
  *F. Dammaß (TU Dresden), K. Friedrichs (TU Dresden), K. Kalina (TU Dresden), M. Kästner (TU Dresden)
• Unsupervised identification of constitutive laws using neural networks in a thermodynamically consistent framework guided by the CRE and mCRE methods
  *N. Klebi (ENS Paris-Saclay - LMPS), L. Chamoin (ENS Paris- Saclay - LMPS)
• Automatic learning of physics-constrained space heterogeneous PDEs for defect identification in slender mechanical structures
  *R. Ferrier (Mines Saint-Etienne), S. Drapier (Mines Saint-Etienne), M. Kadri (CESI-LINEACT)
• Leveraging Data Distillation and Budget Forcing for Automated Constitutive Modeling in Material Science
  *H. Li (University of Cambridge)