Efficient Neural Multigrid Solver for High-Frequency and Heterogeneous Helmholtz Equations

A deep learning-enhanced multigrid solver is introduced to effectively resolve high-wavenumber and heterogeneous Helmholtz equations by partitioning the iterative error into characteristic and non-characteristic components and addressing them separately.