Scheduled presentations:

• When big neural networks are not enough: physics, multifidelity and kernels
  *A. Howard (Pacific Northwest National Laboratory), W. Chen (Pacific Northwest National Laboratory), S. Qadeer (Pacific Northwest National Laboratory), P. Stinis (Pacific Northwest National Laboratory)
• Student Efficient and accurate training of physics-informed deep operator networks with the conjugate kernel
  *A. Howard (Pacific Northwest National Laboratory), *P. Stinis (Pacific Northwest National Laboratory), S. Murphy (University of North Carolina, Charlotte), S. Qadeer (Pacific Northwest National Laboratory), T. Chiang (Pacific Northwest National Laboratory)
• Neural Operator-Based Modeling of Hypersonic Flows Around Varying Geometries
  *A. Peyvan (Brown University), *G. Karniadakis (Brown University)
• Bridging the Gap between Isogeometric Analysis and Deep Operator Learning
  *M. Möller (Delft University of Technology, Department of Applied Mathematics Mekelweg 4, 2628CD Delft)
• Scalable Neural Network Approach for Heat Transport in Groundwater
  *J. Pelzer (University of Stuttgart), M. Schulte (University of Stuttgart)
• DeepONet-based Preconditioning for Krylov Methods
  *A. Kopaničáková (Toulouse-INP)