Scheduled presentations:

• Geometric Neural Operators for Bayesian Inverse Problems and Optimization Under Uncertainty
  *O. Ghattas (The University of Texas at Austin), L. Cao (The University of Texas at Austin), P. Chen (Georgia Tech), D. Luo (The University of Texas at Austin), T. O'Leary-Roseberry (The University of Texas at Austin), U. Villa (The University of Texas at Austin)
• Generating Efficient Randomized Quadrature Rules for 2D and 3D Trunk Spaces using Machine Learning
  *T. Teijeiro (BCAM - Basque Center for Applied Mathematics), D. Pardo (University of the Basque Country (UPV/EHU)), V. Calo (Curtin University)
• ELM type neural networks in scientific computing
  *D. De Falco (Scuola Superiore Meridionale), E. Schiassi (University of Arizona), F. Calabrò (Università "Federico II"), M. Pragliola (Università "Federico II")
• Goal-Oriented Adaptivity for solving Partial Differential Equations using Neural Networks
  *C. Uriarte (BCAM - Basque Center for Applied Mathematics), D. Pardo (UPV/EHU - University of the Basque Country), J. Taylor (CUNEF University), V. Calo (Curtin University), I. Muga (Pontifical Catholic University of Valparaíso)
• Injecting physics into deep learning based reduced order models: methods for exact mass conservation
  *N. Franco (MOX, Politecnico di Milano), W. Boon (MOX, Politecnico di Milano), A. Fumagalli (MOX, Politecnico di Milano), P. Zunino (MOX, Politecnico di Milano)
• Neural Integration for Constitutive Equations in Small Data
  *F. Masi (The University of Sydney), I. Einav (The University of Sydney)