50 citations · 62 across the 4 of their papers we have counts for
5 papers
TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data
Zakaria Elabid, Lena Sasal, Daniel Busby +1
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 forecastin…
TempoKGAT: A Novel Graph Attention Network Approach for Temporal Graph Analysis
Lena Sasal, Daniel Busby, Abdenour Hadid
Graph neural networks (GNN) have shown significant capabilities in handling structured data, yet their application to dynamic, temporal data remains limited. This paper presents a…
Knowledge-Based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows
Zakaria Elabid, Daniel Busby, Abdenour Hadid
Physics-informed neural networks (PINNs) have gained significant prominence as a powerful tool in the field of scientific computing and simulations. Their ability to seamlessly int…
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges
Abdenour Hadid, Tanujit Chakraborty, Daniel Busby
Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impre…
Generating unrepresented proportions of geological facies using Generative Adversarial Networks
Alhasan Abdellatif, Ahmed H. Elsheikh, Gavin Graham +2
In this work, we investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset. The new generated…