Scheduled presentations:

• Keynote Machine Learning for Scientific Discovery: with Examples in Fluid Mechanics
  *S. Brunton (University of Washington)
• Veni, VINDy, Vici: a variational method to build ROMs with embedded uncertainty quantification
  *P. Conti (Politecnico di Milano), J. Kneifl (University of Stuttgart), A. Manzoni (Politecnico di Milano), A. Frangi (Politecnico di Milano), J. Fehr (University of Stuttgart), S. Brunton (University of Washington), N. Kutz (University of Washington)
• Trustworthy Scientific Machine Learning with SINDy and Ensemble Learning
  *U. Fasel (Imperial College London)
• Data-Driven Sparse Sensing and Modeling Under Uncertainty
  *K. Manohar (University of Washington)
• Neural Operator-based symbolic model discovery
  *S. Garmaev (École polytechnique fédérale de Lausanne), O. Fink (École polytechnique fédérale de Lausanne)