6 citations · 13 across the 6 of their papers we have counts for
6 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…
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…
Learning Interface Conditions in Domain Decomposition Solvers
Ali Taghibakhshi, Nicolas Nytko, Tareq Zaman +3
Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requir…
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
Ali Taghibakhshi, Scott MacLachlan, Luke Olson +1
Large sparse linear systems of equations are ubiquitous in science and engineering, such as those arising from discretizations of partial differential equations. Algebraic multigri…