Scheduled presentations:

• Parametric Physics-Informed Neural Networks for Material Model Calibration in Structural Health Monitoring
  D. Anton*, H. Wessels, A. Henkes, U. Römer
• Phase-Field Modeling Of Fracture Processes Using Physics-Informed Deep Learning
  M. Manav*, R. Molinaro, S. Mishra, L. De Lorenzis
• Physics-Informed Neural Networks for Probabilistic Mechanical Analysis: Potentials, Challenges, and Limitations
  E. Hosseini*, H. Xu
• Physics-Constrained NN-based Hyperelastic Constitutive Modeling of TPVs
  H. Lobato*, A. Burgoa, J. Matxain
• Physically Recurrent Neural Networks for Computational Homogenization of Composite Materials with Microscale Debonding
  N. Kovacs*, M. Maia, I. Rocha, C. Furtado, P. Camanho, F. van der Meer
• Why and how to incorporate physical information into artificial neural networks for mechanical problems
  G. Geuken*, P. Kurzeja, J. Mosler
• Physics-Informed Wavelet Neural Operator for Data-free Learning of Parametric Partial Differential Equations
  T. Tripura*, N. N, S. Chakraborty