Scheduled presentations:

• From Pore Geometry to Effective Properties: A Multiphysics Modelling Approach for AM Porous Structures
  *U. Soellner (Siemens AG)
• Accelerating Finite Cell Simulations through Model Order Reduction Techniques
  *J. Vargas-Fajardo (Technical University of Munich), P. Weißinger (Technical University of Munich), E. Gündoğan (Technical University of Munich), F. Duddeck (Technical University of Munich)
• A Bayesian data-driven thermal predictive model of an office building using lumped network
  *Z. Wang (TU Eindhoven), I. Garcia (TU Eindhoven), S. Stoter (TU Eindhoven), C. Verhoosel (TU Eindhoven)
• Projection-Based Reduced Order Modeling for Solid Mechanics: A PyTorch Framework for Geometrically Parametrized 3D Optimization
  *M. Staggl (Graz University of Technology), S. Posch (Graz University of Technology)
• Model Reduction and Surrogating in Multiphysics Simulation Model: A Case Study on Digital Twins for Cryo-Exploration
  *D. Bhattacharya (MBD, RWTH Aachen University), J. Kowalski (MBD, RWTH Aachen University)
• Assessment of three methods to train deep learning models with hybrid data in the context of forging processes
  *M. Pasquelin (LCFC Arts et Metiers Sciences et Technologie), C. Durand (LCFC Arts et Metiers Sciences et Technologie), C. Baudouin (LCFC Arts et Metiers Sciences et Technologie), R. Bigot (LCFC Arts et Metiers Sciences et Technologie)