Scheduled presentations:

• Keynote Theory-to-Practice Gap in Operator Learning
  *M. Trautner (California Institute of Technology (Caltech))
• Combining DL-ROMs and Operator Learning: Towards Efficient And Mesh-Agnostic Surrogates
  *S. Brivio (Politecnico di Milano), G. Kissas (Swiss Data Science Center), A. Manzoni (Politecnico di Milano), S. Mishra (ETH Zurich)
• Approaching the Optimal Closure: Equivariance, Inductive Bias, and Reynolds-Number Generalization in Data-Driven LES
  *S. Agdestein (Centrum Wiskunde & Informatica), B. Sanderse (Centrum Wiskunde & Informatica)
• Universal Optimal Learning of High-Dimensional Anisotropic Sobolev Functions from Point Samples
  B. Adcock (Simon Fraser University), *A. Gupta (Simon Fraser University)
• Holomorphic neural networks for linear elasticity and Laplace problems in 2D and 3D
  *E. Ballini (Aarhus University), M. Calafà (Aarhus University), E. Hovad (Technical University of Denmark), A. Engsig-Karup (Technical University of Denmark), T. Andriollo (Aarhus University)