38 citations · 195 across the 39 of their papers we have counts for
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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…
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…
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…