Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN

Understand

Learning the physical simulation on large-scale meshes with flat Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due to the scaling complexity w.r.t.

• the number of nodes and over-smoothing.
• There has been growing interest in the community to introduce \textit{multi-scale} structures to GNNs for physical simulation.
• However, current state-of-the-art methods are limited by their reliance on the labor-intensive drawing of coarser meshes or building coarser levels based on spatial proximity, which can introduce wrong edges across geometry boundaries.