Scheduled presentations:

• Physics-Informed Training Strategies for Scalable Surrogate Modeling of Complex Fluid Flows.
  *H. Ramachandran (Technical University of Munich), M. Samset (Technical University of Munich), L. Ortner (Technical University of Munich), S. Schmidt (Technical University of Munich), N. Adams (Technical University of Munich)
• Differentiable fluid dynamics for inverse design and the study of dissipative structure
  *J. Belof (Advanced Micro Devices, Inc.)
• Generative surrogate modeling of hydrodynamic instabilities using stochastic interpolants
  *S. Thévenin (Lawrence Livermore National Laboratory), D. Sterbentz (Lawrence Livermore National Laboratory), K. Korner (Lawrence Livermore National Laboratory), A. Briard (CEA, DAM, DIF), B. Gréa (CEA, DAM, DIF), B. Nadiga (Los Alamos National Laboratory), G. Fernández-Godino (Lawrence Livermore National Laboratory), C. Jekel (Lawrence Livermore National Laboratory), W. Schill (Lawrence Livermore National Laboratory), J. Belof (Lawrence Livermore National Laboratory)
• A Discretization-Free Physics-Informed Graph Neural Network for Thermal Flow Estimation
  *K. Shimoyama (Kyushu University), K. Okuhara (Kyushu University)
• A Unified Pressure-Saturation PINN for the Buckley-Leverett Model
  *Y. Kenzhebek (Al-Farabi Kazakh National University), T. Imankulov (Al-Farabi Kazakh National University), S. Bekele (Al-Farabi Kazakh National University), D. Akhmed-Zaki (Mukhtar Auezov South Kazakhstan University)
• Predicting Casting-Induced Shape Distortion Using Hybrid Physics-Informed Neural Networks in Low-Pressure Die Casting
  *S. Adhikari (Institute for Energy Technology), T. Celik (University of Agder), G. Noorsumar (Institute for Energy Technology), D. Mortensen (Institute for Energy Technology), R. Babaei (Aludyne Norway AS)