Scheduled presentations:

• Keynote Efficient training of physics-informed neural operators
  *A. Howard (Pacific Northwest National Laboratory), A. Heinlein (TU Delft), P. Stinis (Pacific Northwest National Laboratory)
• Multi-Level Monte Carlo Training in Operator Learning
  *S. Fresca (University of Washington), J. Rowbottom (University of Cambridge), P. Liò (Univesity of Cambridge), C. Schönlieb (Univesity of Cambridge), N. Boullè (Imperial College London)
• Hybrid Least Squares/Gradient Descent Methods for DeepONets
  J. Choi (KAIST), *C. Lee (KAIST), M. Moon (Korea Military Academy)
• Physics-Informed Laplace Neural Operator for Learning Differential Equations
  *M. CHOI (POSTECH)
• Learning to Initialize: Neural Operator–Based Preconditioning for Lattice Boltzmann Blood Flow Simulations
  A. Junk (Technical University of Munich), *F. Holzberger (Technical University of Munich), S. Lunowa (Technical University of Munich), J. Winter (Technical University of Munich), B. Wohlmuth (Technical University of Munich)