Scheduled presentations:

• Symplectic neural networks
  *B. Tapley (SINTEF)
• Fast process simulations of complex engineering parts using physics-informed graph network simulators (PI-GNS)
  *T. Würth (Karlsruhe Institute of Technology (KIT)), C. Zimmerling (Karlsruhe Institute of Technology (KIT)), N. Freymuth (Karlsruhe Institute of Technology (KIT)), G. Neumann (Karlsruhe Institute of Technology (KIT)), L. Kärger (Karlsruhe Institute of Technology (KIT))
• Gaussian Process Learning of Nonlinear Dynamics
  *D. Ye (University of Twente), M. Guo (University of Twente)
• Learning partial differential equations with pseudo-Hamiltonian neural networks
  *S. Eidnes (SINTEF Digital), K. Lye (SINTEF Digital)
• Data-driven methods in energy transition
  *D. Baroli (USI), M. Multerer (USI), L. Pernigo (USI)
• Randomized neural network with Petrov–Galerkin methods for solving nonlinear PDEs
  Y. Shang (Xi'an Jiaotong University), *F. Wang (Xi'an Jiaotong University), J. Sun (Xi'an Jiaotong University)