Scheduled presentations:

• Keynote Time Integration of Parametrized Partial Differential Equations with Neural Network Surrogate Models
  *M. Kast (EPFL), J. Hesthaven (EPFL)
• A Novel Reduced-Order Model for Advection-Dominated Problems Based on the Radon-Cumulative-Distribution Transform
  *G. Stabile (University of Urbino Carlo Bo), T. Long (University of Nottingham), R. Barnett (University of Nottingham), R. Jefferson-Loveday (University of Nottingham), M. Icardi (University of Nottingham)
• Data-driven Models for Mold-based Processes
  *R. Pohlmann (Vienna University of Technology), A. Gruber (Sandia National Labratories), R. Rao (Sandia National Labratories), N. Trask (University of Pennsylvania), S. Elgeti (Vienna University of Technology)
• Neural Galerkin Schemes That Can Preserve Hamiltonians and Other Quantities
  *P. Schwerdtner (Courant Institute, New York University), P. Schulze (Technische Universität Berlin), J. Berman (Courant Insititute, New York University), B. Peherstorfer (Courant Institute, New York University)
• A Deep Learning Surrogate Model for Reservoir Characterization
  *C. Millevoi (University of Padova), C. Zoccarato (University of Padova), M. Ferronato (University of Padova)