13 citations · 19 across the 11 of their papers we have counts for
5 papers · 1 filter
Advection Augmented Convolutional Neural Networks
Niloufar Zakariaei, Siddharth Rout, Eldad Haber +1
Many problems in physical sciences are characterized by the prediction of space-time sequences. Such problems range from weather prediction to the analysis of disease propagation a…
Graph Neural Reaction Diffusion Models
Moshe Eliasof, Eldad Haber, Eran Treister
The integration of Graph Neural Networks (GNNs) and Neural Ordinary and Partial Differential Equations has been extensively studied in recent years. GNN architectures powered by ne…
An Over Complete Deep Learning Method for Inverse Problems
Moshe Eliasof, Eldad Haber, Eran Treister
Obtaining meaningful solutions for inverse problems has been a major challenge with many applications in science and engineering. Recent machine learning techniques based on proxim…
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
Moshe Eliasof, Eldad Haber, Eran Treister +1
Graph Neural Networks (GNNs) have demonstrated remarkable success in modeling complex relationships in graph-structured data. A recent innovation in this field is the family of Dif…
pathGCN: Learning General Graph Spatial Operators from Paths
Moshe Eliasof, Eldad Haber, Eran Treister
Graph Convolutional Networks (GCNs), similarly to Convolutional Neural Networks (CNNs), are typically based on two main operations - spatial and point-wise convolutions. In the con…