Scheduled presentations:

• Learning from AI: The Case for Differentiable PDE Solvers
  *N. Thuerey (TUM), B. List (TUM), A. Franz (TUM), L. Guastoni (TUM), G. Baldan (TUM), R. Winchenbach (TUM)
• Automatic Differentiation of Adaptive Solvers Does Not Always Converge to the Correct Derivatives
  *C. Rackauckas (JuliaHub, MIT)
• Characterizing Extreme Events in Dynamical Systems through Sensitivity-Based Modal Decomposition
  *N. Zolman (University of Washington), S. Mokbel (University of Washington), S. Otto (Cornell University), S. Brunton (University of Washington)
• Jutul.jl: fully differentiable finite-volume simulation in Julia
  O. Møyner (SINTEF Digital), *Ø. Klemetsdal (SINTEF Digital), A. Brostrøm (SINTEF Digital), S. Krogstad (SINTEF Digital), K. Lie (SINTEF Digital)
• Differentiable Finite Element Modeling of Reactive Low-Mach Flows
  *B. Hofer (hermo-Fluid Dynamics Group; Technical Univers), M. Désor (hermo-Fluid Dynamics Group; Technical Univers), A. Zimmermann (hermo-Fluid Dynamics Group; Technical Univers), W. Polifke (hermo-Fluid Dynamics Group; Technical Univers)
• Direct differentiation of the structural response in a mixed Lagrangian time-stepping framework
  *N. Pollini (Technion - Israel Institute of Technology)