6 citations · 13 across the 6 of their papers we have counts for
3 papers · 1 filter
Learning from Integral Losses in Physics Informed Neural Networks
Ehsan Saleh, Saba Ghaffari, Timothy Bretl +2
This work proposes a solution for the problem of training physics-informed networks under partial integro-differential equations. These equations require an infinite or a large num…
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman +3
Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants. These solvers rely…
Generalizing Lloyd's algorithm for graph clustering
Tareq Zaman, Nicolas Nytko, Ali Taghibakhshi +3
Clustering is a commonplace problem in many areas of data science, with applications in biology and bioinformatics, understanding chemical structure, image segmentation, building r…