Scheduled presentations:

• Non-iterative full-field material parameter identification using forward neural networks
  *S. Brabender (TU Dortmund), L. Rose (TU Dortmund), A. Menzel (TU Dortmund)
• Conditional Flow Matching for the Solution of Probabilistic Inverse Problems
  *A. Oberai (University of Southern California), A. Fardisi (University of Southern California), M. Aminy (University of Southern California), B. Binder (University of Southern California), B. Shaddy (University of Southern California)
• Differentiable physics for the inverse design of frictional metainterfaces: beyond the hertzian limit
  *J. Bilotto (EPFL), A. Singhal (Brown University), G. Cortes (EPFL), J. Garcia-Suarez (EPFL), J. Molinari (EPFL)
• Physics-Informed Neural Network for Unsteady Flow Simulation and Their Applications to Shape Optimization
  A. Hirayama (The University of Tokyo), S. Watanabe (Komatsu Ltd.), *Y. Yugeta (The University of Tokyo), T. Endo (The University of Tokyo), M. Liu (CISS, IIS, The University of Tokyo), Y. Hasegawa (CISS, IIS, The University of Tokyo)
• Leviathan: a Belief–Flow World Model for Forward Prediction and Inverse Control
  *R. Batley (Virginia Polytechnic Institute and State Univ), S. Saha (Virginia Tech)
• Parameterized Physics-Informed Neural Network for Elastic Material Modeling
  *Z. Będkowska (Akademia Górniczo-Hutnicza)