Acceleration of an algebraic multigrid pressure solver using graph neural networks
Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities. This work introduces a data-driven algebraic multigrid (AMG) smoother that uses a modified graph convolutional isomorphism network (GCIN). The graph neural network predicts optimal polynomial coefficients to construct a sparse pseudo-inverse operator across diverse grid topologies. The coefficients are optimized to reduce the residual after each V-cycle iteration. By directly capturing the algebraic structure of the system from the sparse coefficient matrix, the propose...
| Authors | Eric Chillón, Artur K. Lidtke, Nguyen Anh Khoa Doan et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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