Scheduled presentations:

• Computational Homogenization of Arbitrary Heterogeneous Mesostructures with Inelastic Constitutive Behavior using Machine Learning Methods
  *J. Stöcker (Technische Universität Dresden), E. Elsayed (Leibniz Universität Hannover), F. Aldakheel (Leibniz Universität Hannover), M. Kaliske (Technische Universität Dresden)
• Graph-based Representation of Elastoplastic Material Response in the Data-driven Framework
  *H. Dandin (GeM), A. Leygue (GeM), L. Stainier (GeM)
• A Neural Network Based Strategy for the Modelling of Constitutive Behaviour of Solids Applied to Porous Elasto-Plastic Material
  *R. Alhayki (Swansea University), E. Muttio (Swansea University), W. Dettmer (Swansea University), D. Perić (Swansea University)
• Automated, machine learning based surface crack detection in fracture experiments
  *N. Karathanasopoulos (New York University), P. Hadjidoukas (University of Patras), D. Mohr (ETH)
• A Neural Network Based Surrogate Model for Inelastic Solid Materials Simulations
  *E. Muttio Zavala (Swansea University), R. Alhayki (Swansea University), W. Dettmer (Swansea University), D. Peric (Swansea University)
• Machine Learning and Data Mining of Dislocation Simulations
  *A. Demirci (IAS-9, Forschungszentrum Jülich GmbH), S. Sandfeld (IAS-9, Forschungszentrum Jülich GmbH)