Zakaria Elabid

Sorbonne University Abu Dhabi

Accurately forecasting dynamic processes on graphs, such as traffic flow or disease spread, remains a challenge. While Graph Neural Networks (GNNs) excel at modeling and forecasting spatio-temporal data, they often lack the ability to directly incorporate underlying physical laws. This work presents TG-PhyNN, a novel Temporal Graph Physics-Informed...

Representativity and accuracy of digital rock physics (DRP) simulations depend strongly on the size of the image volume and the resolution obtained. Even with one of the fastest DRP simulation techniques like pore network modelling, simulation volumes have typically been limited to few cubic millimetres for highly resolved images. In this paper, a...

Deep Learning has received increased attention due to its unbeatable success in many fields, such as computer vision, natural language processing, recommendation systems, and most recently in simulating multiphysics problems and predicting nonlinear dynamical systems. However, modeling and forecasting the dynamics of chaotic systems remains an open...