Scheduled presentations:

• A Data-Driven Rheological Model Based on Input Convex Neural Networks
  *N. Parolini (Politecnico di Milano)
• Using Neural-Networks to Close Reynolds-Averaged Navier-Stokes Equations
  *D. Oberto (Politecnico di Torino), S. Berrone (Politecnico di Torino), M. Strazzullo (Politecnico di Torino)
• Optimizing Physics-Informed Neural Networks for Pulsatile Non-Newtonian Channel Flows
  *N. Park (Seoul National University), J. Son (Seoul National University), J. Nam (Seoul National University)
• Anisotropic mesh adaptation techniques for the Variational MultiScale-Smagorinsky modeling of high Reynolds flows
  *E. Temellini (Politecnico di Milano), N. Ferro (Politecnico di Milano), G. Stabile (Università degli Studi di Urbino Carlo Bo), S. Perotto (Politecnico di Milano), T. Chacon Rebollo (Universidad De Sevilla)
• Large eddy simulation of an excited nitrogen-diluted hydrogen flame stabilised by a bluff-body
  L. Caban (Czestochowa University of Technology), A. Wawrzak (Czestochowa University of Technology), *A. Tyliszczak (Czestochowa University of Technology)