Scheduled presentations:

• Parsimonious PINN-PGD with full domain decomposition capabilities
  *C. Ghnatios (Arts et métiers institute of technology), F. Chinesta (Arts et métiers institute of technology)
• Physics informed Graph Neural Networks – towards real-time applications for haptic devices
  *P. Martins (Aragon Institute in Engineering Research), B. Moya (CNRS@CREATE), F. Chinesta (École Nationale Supérieure d’Arts et Métiers), E. Cueto (Aragon Institute in Engineering Research)
• New treatment of the boundary conditions in Deep-Galerkin/PINNs methods: application to high dimensional non-linear parabolic PDEs
  *J. García Rodríguez (Univerisdad de A Coruña), J. Pérez Villarino (Univerisdad de A Coruña), Á. Leitao (Univerisdad de A Coruña)
• Learning Thermodynamically Consistent Master Equations for Open Quantum Systems
  *P. Sentz (Brown University), S. Günther (Lawrence Livermore National Laboratory), B. Keith (Brown University)
• Large-scale Statistical Learning for Mass Transport Prediction in Porous Materials Using 90,000 Artificially Generated Microstructures
  *B. Prifling (Ulm University), M. Röding (Research Institutes of Sweden), P. Townsend (University of East Anglia), M. Neumann (Ulm University), V. Schmidt (Ulm University)