Scheduled presentations:

• Spectrally Constrained Fourier Neural Operators (SC-FNO) for Incompressible Vortex-Dominated Flows
  *L. Achour (Arts et Métiers ParisTech, CNRS, CNAM), S. Rodriguez Iturra (Arts et Métiers ParisTech, CNRS, CNAM), B. Moya Garcia (Arts et Métiers ParisTech, CNRS, CNAM), F. Chinesta (Arts et Métiers ParisTech, CNRS, CNAM)
• Physics-Aware Generative Modeling of Hypersonic Aerodynamics
  *F. Sestak (Emmi AI), D. Rubini (Emmi AI), D. Bezgin (Technical University of Munich), A. Buhendwa (Technical University of Munich), L. Serrano (Emmi AI), R. Kurle (Emmi AI), F. Paischer (Emmi AI, JKU Linz), J. Brandstetter (Emmi AI, JKU Linz), S. Kaltenbach (Emmi AI), N. Adams (Technical University of Munich)
• Physics-Constrained Machine Learning For Compressor Speedline Prediction Across The Operating Range
  *D. Singh (Deutsches Zentrum für Luft- und Raumfahrt e.V), M. Lockan (Deutsches Zentrum für Luft- und Raumfahrt e.V), R. Schaffrath (Deutsches Zentrum für Luft- und Raumfahrt e.V)
• Reliable, Fast and Stable Inverse Analysis of Incompressible Flow With PINN Enhanced With Normalized Distance and Unbiased Weights
  *S. Deguchi (University of Tsukuba), N. Mitsume (University of Tsukuba), M. Asai (Kyushu University)
• 3D PINN Models of Stirred Tank Reactors: What Works, What Doesn't, and at What Cost?
  *V. Trávníková (RWTH Aachen University), E. von Lieres (FZ Jülich), M. Behr (RWTH Aachen University)