Scheduled presentations:

• Machine-Learning-Based Surrogate Material Models For Elasto-Plasticity And Elasto-Damage
  *C. Fricke (ILSB TU Wien), M. Luxner (Luxner Engineering ZT GmbH), L. Peyker (Luxner Engineering ZT GmbH), L. Mitrovic (TU Wien), H. Pettermann (ILSB TU Wien)
• Effective application of hybrid material modeling for topology optimization of multiphase hyperelastic composites
  *H. Vijayakumaran (Delft University of Technology), J. Russ (Johns Hopkins University Applied Physics Lab), G. Paulino (Princeton University), M. Bessa (Brown University)
• A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations
  *J. Storm (Delft University of Technology), I. Rocha (Delft University of Technology), F. van der Meer (Delft University of Technology)
• Improving the Production Accuracy of the Tailored Fiber Placement Process Through a Machine-Learning Algorithm
  *L. Bittrich (Leibniz-Institut für Polymerforschung Dresden), E. de Menezes (Leibniz-Institut für Polymerforschung Dresden), M. Woestmann (Faserinstitut Bremen e.V.), A. Miene (Faserinstitut Bremen e.V.), L. Echer (SENAI Institute of Innovation in Polymer Eng.), A. Spickenheuer (Leibniz-Institut für Polymerforschung Dresden)
• Machine learning model for correlating microstructural features and macroscopic properties of heterogeneous composites
  C. Shen (University of Chinese Academy of Sciences), *H. Zhao (University of Chinese Academy of Sciences), R. Mu (University of Chinese Academy of Sciences), A. Wang (University of Chinese Academy of Sciences), K. Wang (University of Chinese Academy of Sciences)