Scheduled presentations:

• Δ-PINN: Physics-Informed Neural Networks on Complex Geometries
  S. Pezzuto*, F. Sahli Costabal, P. Perdikaris
• Training Gives Me PINNS and Needles - On the Complexity of Training Physics-Informed Neural Networks
  F. Rohrhofer*, S. Steger, S. Posch, C. Gößnitzer, B. Geiger
• Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks
  L. Pegolotti*, M. Pfaller, N. Rubio, K. Ding, R. Brugorolas Brufau, E. Darve, A. Marsden
• Parametric PDEs solvers, uncertainty quantification and parameter estimation
  S. Riffaud*, D. Lombardi, M. Fernandez
• Machine Learning-Based Method for Structural Characterization and Fracture Risk Evaluation of Vertebrae With Metastasis.
  B. Gandía-Vañó*, X. García-Andrés, E. Arana, E. Nadal-Soriano, J. Ródenas-García
• Level-set geometry parametrization and kernel optimization for predictions of thrombectomy outcomes
  S. Bridio*, G. Luraghi, F. Migliavacca, D. González, P. Díez, J. Rodriguez Matas, A. García-González