Scheduled presentations:

• Keynote On solving/learning nonlinear PDEs with GPs
  *H. Owhadi (Caltech)
• Scalable Neural Wavefunctions for Quantum Monte Carlo
  *J. Richter Powell (Vector Institute)
• Resolution Invariant Generative Models for Functional Data
  *J. Seidman (University of Pennsylvania), G. Kissas (University of Pennsylvania), G. Pappas (University of Pennsylvania), P. Perdikaris (University of Pennsylvania)
• Competitive Physics-Informed Networks
  Q. Zeng (Georgia Institute of Technology), Y. Kothari (Georgia Institute of Technology), S. Bryngelson (Georgia Institute of Technology), *F. Schaefer (Georgia Institute of Technology)
• Scientific machine learning models for cardiovascular computational medicine
  *S. Pagani (Politecnico di Milano), F. Regazzoni (Politecnico di Milano), L. Dede' (Politecnico di Milano), A. Quarteroni (Politecnico di Milano, EPFL)
• Combining data and models to enable surrogate-based automatic discovery of engineering solutions.
  *R. Pestourie (MIT)