Scheduled presentations:

• Using Neural Physics for Constructing Finite Element Solution Methods: NN4FEM
  *T. Yang (Imperial College London), J. Xiang (Imperial College London), B. Chen (Imperial College London), C. Pain (Imperial College London)
• A Learnable Multigrid Framework via Graph Convolutions
  *X. Wang (University of Macau), L. Luo (University of Macau)
• Improving the stability and accuracy of discontinuous Galerkin schemes using neural networks
  L. Bois (Inria), E. Franck (Inria), V. Michel-Dansac (Inria), *L. Navoret (University of Strasbourg), V. Vigon (University of Strasbourg)
• Coupling Phisically Informed Graph Neural Networks with External Solvers
  *N. Picchiarelli (Sant'Anna School of Advanced Studies), R. Halder (SISSA), G. Stabile (Sant'Anna School of Advanced Studies)
• NewPINNs: Solver-Guided Training of Neural Networks for Differential Equations
  S. Chandran (University of California Riverside), M. Makki (University of California Riverside), R. Narkhede (Procter & Gamble Service GmbH), M. Raissi (University of California Riverside), A. Grenier (Procter & Gamble service GmbH), *B. Mohebbi (Procter & Gamble Service GmbH)
• Partitioned neural network approximation to PDEs with a Robin interface condition
  *H. Kim (Kyung Hee University), Y. Jeon (Kyung Hee University)