Scheduled presentations:

• Δ-PINN: Physics-Informed Neural Networks on Complex Geometries
  *S. Pezzuto (Università di Trento), F. Sahli Costabal (Pontificia Universidad Católica de Chile), P. Perdikaris (University of Pennsylvania)
• Training Gives Me PINNS and Needles - On the Complexity of Training Physics-Informed Neural Networks
  *F. Rohrhofer (Know-Center GmbH), S. Steger (Graz University of Technology), S. Posch (LEC GmbH), C. Gößnitzer (LEC GmbH), B. Geiger (Know-Center GmbH)
• Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks
  *L. Pegolotti (Stanford University), M. Pfaller (Stanford University), N. Rubio (Stanford University), K. Ding (Intel Corporation), R. Brugorolas Brufau (Intel Corporation), E. Darve (Stanford University), A. Marsden (Stanford University)
• Parametric PDEs solvers, uncertainty quantification and parameter estimation
  *S. Riffaud (Inria Paris), D. Lombardi (Inria Paris), M. Fernandez (Inria Paris)
• Machine Learning-Based Method for Structural Characterization and Fracture Risk Evaluation of Vertebrae With Metastasis.
  *B. Gandía-Vañó (Universitat Politècnica València (I2MB - UPV)), X. García-Andrés (Universitat Politècnica València (I2MB - UPV)), E. Arana (Fundación instituto valenciano de oncología), E. Nadal-Soriano (Universitat Politècnica València (I2MB - UPV)), J. Ródenas-García (Universitat Politècnica València (I2MB - UPV))
• Level-set geometry parametrization and kernel optimization for predictions of thrombectomy outcomes
  *S. Bridio (Politecnico di Milano), G. Luraghi (Politecnico di Milano), F. Migliavacca (Politecnico di Milano), D. González (Universidad de Zaragoza), P. Díez (Universitat Politècnica de Catalunya), J. Rodriguez Matas (Politecnico di Milano), A. García-González (Universitat Politècnica de Catalunya)