Scheduled presentations:

• Leveraging Data Distillation and Budget Forcing for Automated Constitutive Modeling in Material Science
  *H. Li (University of Cambridge)
• Machine Learning-Aided Inverse Design of Porous Metamaterials
  *F. Aldakheel (Leibniz Universitaet Hannover), P. THIEN NGUYEN (Leibniz Universitaet Hannover), Y. HEIDER (Leibniz Universitaet Hannover)
• New Insights Into Strain-Induced Rubber Crystallisation and Fracture: A Precise and Efficient Physics-Augmented Neural Network Approach
  *F. Dammaß (TU Dresden), K. Friedrichs (TU Dresden), K. Kalina (TU Dresden), M. Kästner (TU Dresden)
• Variational Physics Informed Neural Networks formulation for solid mechanics
  *D. Rodopoulos (New York University Abu Dhabi), P. Pantidis (New York University Abu Dhabi), N. Karathanasopoulos (New York University)
• Multiscale modeling of metamaterials and complex composite structures using the Empirical Interscale Finite Element Method (EIFEM)
  *J. HERNÁNDEZ (CIME), E. SOUDAH (CIMNE)
• Unsupervised identification of constitutive laws using neural networks in a thermodynamically consistent framework guided by the CRE and mCRE methods
  *N. Klebi (ENS Paris-Saclay - LMPS), L. Chamoin (ENS Paris- Saclay - LMPS)