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Found 4 papers, 4 papers with code
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants.
Optimized Sparse Matrix Operations for Reverse Mode Automatic Differentiation
Sparse matrix representations are ubiquitous in computational science and machine learning, leading to significant reductions in compute time, in comparison to dense representation, for problems that have local connectivity.
Learning Interface Conditions in Domain Decomposition Solvers
Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations.
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
A system of linear equations defines a graph on the set of unknowns and each level of a multigrid solver requires the selection of an appropriate coarse graph along with restriction and interpolation operators that map to and from the coarse representation.
