Scheduled presentations:

• Controlling Bias and Variance When Integrating Neural Networks for Solving PDEs
  *J. Taylor (CUNEF Universidad), D. Pardo (UPV/EHU)
• A novel variance-based loss function for improved physics-informed deep learning optimization
  *J. Hanna (Inria), H. Talbot (CentraleSupelec), I. Vignon Clementel (Inria)
• Machine Learning-Derived Stochastic Quadrature Rules for High-Order, Multidimensional Spaces with Unbiasedness Guarantees
  T. Teijeiro (Basque Center for Applied Mathematics), J. Taylor (CUNEF University), *D. Pardo (University of the Basque Country (UPV/EHU))
• Multi-Integral Importance Sampling for Solving PDEs Using Neural Networks
  *X. Otxandorena (University of the Basque Country), D. Pardo (University of the Basque Country), M. Croci (Basque Center for Applied Mathematics)
• Inexact Uzawa- Double Deep Ritz Method for Weak Adversarial Neural Networks
  *E. Benny Chacko (University of Nottingham), I. Brevis (University of Nottingham), L. Espath (University of Nottingham), K. G. van der Zee (University of Nottingham)
• RUNNs: Ritz-Uzawa Neural Networks for Solving Variational Problems
  *P. Herrera (Basque Center For Applied Mathematics (BCAM)), I. Muga (Pontificia Universidad Católica de Valparaiso), J. Taylor (CUNEF Universidad), C. Uriarte (Curtin University), K. Van Der Zee (University of Nottingham), D. Pardo (University of the Basque Country (UPV/EHU))