649 citations · 960 across the 22 of their papers we have counts for
22 papers
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…
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching
Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal +3
Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity. It involves identifying and linking different d…
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…
Generalizing Reduction-Based Algebraic Multigrid
Tareq Zaman, Nicolas Nytko, Ali Taghibakhshi +3
Algebraic Multigrid (AMG) methods are often robust and effective solvers for solving the large and sparse linear systems that arise from discretized PDEs and other problems, relyin…
Optimized Sparse Matrix Operations for Reverse Mode Automatic Differentiation
Nicolas Nytko, Ali Taghibakhshi, Tareq Uz Zaman +3
Sparse matrix representations are ubiquitous in computational science and machine learning, leading to significant reductions in compute time, in comparison to dense representation…