Scheduled presentations:

• Data-driven Neural Networks for Windkessel Parameter Calibration
  *B. Hoock (TUM), T. Köppl (Fraunhofer FOKUS)
• Handling Shape Variability in Surrogate Cardiovascular Models
  F. Romor (WIAS Berlin), F. Galarce (PUC Valparaiso), *A. Caiazzo (WIAS Berlin)
• Data-Driven Surrogate Models for Aortic Hemodynamics Under Shape Variability
  *R. Schussnig (TU Graz and UT Austin), F. Romor (WIAS Berlin), A. Caiazzo (WIAS Berlin), G. Holzapfel (TU Graz and NTNU Trondheim)
• Wave Inversion for Magnetic Resonance Elastography Using Physics-Trained Neural Networks
  *H. Bustin (Charité – Universitätsmedizin Berlin), T. Meyer (Charité – Universitätsmedizin Berlin), J. Jordan (Charité – Universitätsmedizin Berlin), L. Walczak (Charité – Universitätsmedizin Berlin), H. Tzschätzsch (Charité – Universitätsmedizin Berlin), I. Sack (Charité – Universitätsmedizin Berlin), A. Hennemuth (Charité – Universitätsmedizin Berlin)
• Fast and Accurate Reconstruction of 3D Cardiac Displacement Fields from Sparse MRI-like Data via PBDW
  *F. Mantegazza (University of Graz), F. Caforio (University of Graz), M. Gsell (Medical University of Graz), C. Augustin (Medical University of Graz), G. Haase (University of Graz), E. Karabelas (University of Graz)