A Deep Learning algorithm to accelerate Algebraic Multigrid methods in Finite Element solvers of 3D elliptic PDEs. (arXiv:2304.10832v1 [math.NA])

Algebraic multigrid (AMG) methods are among the most efficient solvers for
linear systems of equations and they are widely used for the solution of
problems stemming from the discretization of Partial Differential Equations
(PDEs). The most severe limitation of AMG methods is the dependence on
parameters that require to be fine-tuned. In particular, the strong threshold
parameter is the most relevant since it stands at the basis of the construction
of successively coarser grids needed by the AMG methods. We …

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