A modified graph convolutional isomorphism network predicts polynomial coefficients for a sparse pseudo-inverse AMG smoother, cutting V-cycles and delivering 4-37% wall-clock speedups while generalizing to larger and unseen meshes.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Kirigami parachutes are stable for deployments equal to their radius and tumble for smaller ones, as found in effectively infinite-domain fluid simulations.
An LLM-based self-evolving agent discovers a traveling-wave controller with body-frame guidance and yaw feedback that generalizes to unseen targets for an underactuated fluid swimmer.
Introduces SynDRoM and a bounded kinetic viscosity limiter to enable stable sharp-interface simulations of energetic multiphase flows with large density and viscosity ratios.
citing papers explorer
-
Acceleration of an algebraic multigrid pressure solver using graph neural networks
A modified graph convolutional isomorphism network predicts polynomial coefficients for a sparse pseudo-inverse AMG smoother, cutting V-cycles and delivering 4-37% wall-clock speedups while generalizing to larger and unseen meshes.