8/6/22    16:30 - 18:30
Deep Learning in Scientific Computing III  
Minisymposium organized by Manuel Jesus Castro Diaz, Siddharta Mishra and David Pardo
Room: Lounge A2
Chair: Manuel J. Castro
CoChair: David Pardo
A machine learning minimal residual method for solving quantities of interest of parametric PDEs
Ignacio Brevis, Ignacio Muga, David Pardo, Oscar Rodríguez and Kristoffer G. van der Zee

Using Graph Neural Network for gas-liquid interface reconstruction in Volume Of Fluid methods
Michele-Alessandro BUCCI, Jean-Marc GRATIEN, Thibault FANEY and Tamon NAKANO

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models
Federico Fatone, Stefania Fresca and Andrea Manzoni

Parameter estimation for differential problems through multi-fidelity physics-informed neural networks
Francesco Regazzoni, Stefano Pagani, Alessandro Cosenza, Alessandro Lombardi and Alfio Quarteroni

A collocation method based on single-layer feedforward neural network for the resolution of Elliptic PDEs
Francesco Calabrò

A Physics-Informed Deep Learning approach to computing solutions of hyperbolic problems
Rafael Carniello, João Florindo and Eduardo Abreu

A novel Machine Learning method for accurate and real-time numerical simulations of cardiac electromechanics
Luca Dede‘, Francesco Regazzoni, Matteo Salvador and Alfio Quarteroni