Graph Neural Multilevel Preconditioners for Iterative Solvers
This work proposes a Graph Neural Multilevel Preconditioner (GMP) that adopts an AMG hierarchy as a structural prior and learns smoothing, restriction, and interpolation operators in a unified framework and highlights both the promise and the limitations of enforcing AMG-style multilevel structure in learned preconditi...