Scheduled presentations:

• Keynote Statistical Learning Theory for Neural Operators
  N. Reinhardt (Heidelberg University), S. Wang (HU Berlin), *J. Zech (Heidelberg University)
• Student Data-Driven Dimension Reduction Through Symmetry-Promoting Regularization
  *N. Zolman (University of Washington), S. Otto (Cornell University), J. Kutz (University of Washington), S. Brunton (University of Washington)
• Student Graph Neural Diffusion for Accelerated Adaptive Mesh Refinement in Finite Element Methods
  *J. Rowbottom (University of Cambridge)
• Student Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems
  *M. Caldana (Politecnico di Milano), J. Hesthaven (Karlsruhe Institute of Technology)
• Student A Parametric Framework for Kernel-Based Dynamic Mode Decomposition Using Deep Learning
  *K. Kevopoulos (Delft University of Technology), D. Ye (University of Twente)