6 citations · 11 across the 7 of their papers we have counts for
7 papers
Unstructured to structured: geometric multigrid on complex geometries via domain remapping
Nicolas Nytko, Scott MacLachlan, J. David Moulton +3
For domains that are easily represented by structured meshes, robust geometric multigrid solvers can quickly provide the numerical solution to many discretized elliptic PDEs. Howev…
Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation
Nicolas Nytko, Andrew Reisner, J. David Moulton +2
Legacy codes are in ubiquitous use in scientific simulations; they are well-tested and there is significant time investment in their use. However, one challenge is the adoption of…
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…
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…