Scheduled presentations:

• Symplectic neural networks
  B. Tapley*
• Fast process simulations of complex engineering parts using physics-informed graph network simulators (PI-GNS)
  T. Würth*, C. Zimmerling, N. Freymuth, G. Neumann, L. Kärger
• Gaussian Process Learning of Nonlinear Dynamics
  D. Ye*, M. Guo
• Learning partial differential equations with pseudo-Hamiltonian neural networks
  S. Eidnes*, K. Lye
• Data-driven methods in energy transition
  D. Baroli*, M. Multerer, L. Pernigo
• Randomized neural network with Petrov–Galerkin methods for solving nonlinear PDEs
  Y. Shang, F. Wang*, J. Sun