Scheduled presentations:

• On the Formulation of Data-Assisted Constitutive Models for Non-Linear Solid Mechanics
  *A. Cornejo (Universitat Politècnica de Catalunya, DECA-ETSECCPB Centre Internacional de Mètodes Numèrics a l’Enginyeria (CIMNE)), R. Rossi (Universitat Politècnica de Catalunya, DECA-ETSECCPB Centre Internacional de Mètodes Numèrics a l’Enginyeria (CIMNE))
• Physics-augmented neural network material models for hyperelasticity with softening
  J. Fey (TU Darmstadt), D. Klein (TU Darmstadt), *O. Weeger (TU Darmstadt)
• Surrogate Modeling of Crystal Plasticity Using Graph Neural Networks for Microstructural Design
  *A. Safi (Helmholtz-Zentrum Hereon), P. Campos (Helmholtz-Zentrum Hereon), F. Bock (Helmholtz-Zentrum Hereon), B. Klusemann (Helmholtz-Zentrum Hereon, Leuphana University)
• AtomProNet: Data flow to and from machine learning interatomic potentials in materials science
  M. Galib (The University of British Columbia), M. Isiet (The University of British Columbia), *M. Ponga (The University of British Columbia)
• Investigation of a Machine Learning-assisted Material Point Method for Buckling Analysis
  *M. Suzuki (Keio University), M. Muramatsu (Keio University)
• 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)