Scheduled presentations:

• Acoustic scattering off a cylinder: surrogate modeling and inversion via physics-informed neural networks
  S. Razi (University of Massachusetts Dartmouth), D. Amini (MIT), Y. Chen (University of Massachusetts Dartmouth), A. Amirkhizi (University of Massachusetts Lowell), A. Louhghalam (University of Massachusetts Lowell), *M. Tootkaboni (University of Massachusetts Dartmouth)
• Graph Neural Networks for Stress Prediction in Structural Design
  *J. Ribeiro (MIT & University of Porto), B. Ribeiro (TU Delft & Brown University), S. Tavares (University of Aveiro), F. Ahmed (MIT)
• ViTO: Vision Transformer Operator
  *O. Ovadia (Tel Aviv University), A. Kahana (Brown University), P. Stinis (Pacific Northwest National Lab), E. Turkel (Tel Aviv University), D. Givoli (Technion - Israel Institute of Technology), G. Karniadakis (Brown university)
• Semi-mechanistic Prediction of Residence Time and Mixing Dynamics using a Machine Learning-based Convection-Diffusion Model
  A. D (Rutgers University), U. Patil (Rutgers University), E. Olofsson (Danish Technical University), J. Hattel (Danish Technical University), *R. Ramachandran (Rutgers University)
• Neural Fields for Inversion Problems
  *J. Torben (SINTEF), A. Brostrøm (SINTEF)
• Automotive motor rotor design synthesis using conditional Wasserstein Generative Adversarial Networks with gradient penalty and distortion penalty
  *N. Kato (The University of Tokyo), K. Suzuki (AISIN CORPORATION), Y. kondo (AISIN CORPORATION), K. Suzuki (The University of Tokyo), K. Yonekura (The University of Tokyo)