Scheduled presentations:

• Bridging Scientific Machine Learning methods with physics-based models in Computational Cardiology
  *F. Regazzoni (Politecnico di Milano), S. Pagani (Politecnico di Milano), M. Salvador (Stanford University), L. Dede' (Politecnico di Milano), A. Quarteroni (Politecnico di Milano)
• Machine learning and statistical shape modelling for real-time prediction of stent deployment in realistic anatomies
  *B. Bisighini (Mines Saint-Etienne), M. Aguirre (Laboratori de Càlcul Numèric, Universitat Pol), B. Pierrat (Mines Saint-Etienne), S. Avril (Mines Saint-Etienne)
• Efficient Reduced Order Modeling for Coupled Problems in Cardiac Electrophysiology
  *E. Zappon (Medical University of Graz), A. Manzoni (Politecnico di Milano), A. Quarteroni (Politecnico di Milano)
• Taming the Computational Burden: PBDW for Efficient State Estimation
  *F. Mantegazza (University of Graz), F. Caforio (University of Graz), E. Karabelas (University of Graz)
• Constructive non-linear model order reduction for parametric Fluid-Structure Interaction in uncertain and moving domains.
  *D. Lombardi (Inria Paris), M. Fernandez (Inria Paris), S. Riffaud (Inria Paris)
• Comprehensive risk assessment of patients following type-A aortic dissection surgery: A shape analysis and machine learning approach
  *J. Reddy (AIMR, Tohoku University), T. Watanabe (Medical Corporation Shimada Clinic Clover), T. Hayashi (Department of Cardiovascular Surgery, AMC), H. Suito (AIMR, Tohoku University)
• A non-overlapping optimization-based domain decomposition approach to component-based model reduction of incompressible flows
  *L. Zhang (Tongji University), T. Taddei (Inria Bordeaux Sud-Ouest), X. Xu (Tongji University, Chinese Academy of Science)