Variational Physics Informed Neural Networks: an a priori error estimate
Stefano Berrone , Claudio Canuto and Moreno Pintore

Parametric Compressible Flow Predictions using Physics-Informed Neural Networks
Simon Wassing, Stefan Langer and Philipp Bekemeyer

On quadrature rules for solving Partial Differential Equations with Neural Networks
Jon Ander Rivera, Ángel Javier Omella, Jamie M. Taylor and David Pardo

A Deep r-Adaptive Mesh Method for solving Partial Differential Equations
Ángel J. Omella and David Pardo

Accelerating High Order Discontinuous Galerkin solvers using neural networks
Esteban Ferrer, Fernando Manrique de Lara and Kheir-eddine Otmani

A Generative Adversarial Networks approach for solving Partial Differential Equations
Carlos Uriarte, David Pardo, Judit Muñoz-Matute and Ignacio Muga

Geosteering using Deep Learning
Mostafa Shahriari, David Pardo and Jon Ander Rivera

Learning Operators via Mesh-Informed Neural Networks
Nicola R. Franco, Andrea Manzoni and Paolo Zunino

Can deep learning diagnose neurodegenerative diseases with retinal ganglion cell layer?
Alberto Montolío, José Cegoñino, Elena Garcia-Martin and Amaya Pérez del Palomar

Enhanced Bayesian model updating for structural health monitoring via deep learning
Matteo Torzoni, Andrea Manzoni and Stefano Mariani

Damage detection in bridge structures using an unsupervised Deep Autoencoder
Ana Fernandez-Navamuel, Diego Zamora-Sánchez, David Garcia-Sánchez, Filipe Magalhaes and David Pardo

Deep learning methods for liquid crystal driven transformation optics
Jamie M. Taylor, Guilhem Poy, Miha Ravnik and Arghir Zarnescu

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, Tamon NAKANO and Guillaume CHARPIAT

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 PINN computational study for a scalar 2D inviscid Burgers model with Riemann data
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