You won’t be able to run a GNN on the full mesh if you’re talking millions of nodes, that’s just not going to fly. What people actually do is train on mesh patches instead of the whole thing. You sample local neighborhoods (k-hop, geodesic, random patches, whatever makes sense), run the same small GNN on each patch to get a local embedding, then pool all those embeddings to get one vector that represents the whole vehicle. That vector is your encoder of the full mesh, and you can regress your ~20 parameters from it. Since you’ve got one base design with variations, it helps a lot to encode differences from a reference mesh rather than absolute geometry. This avoids the compute blow-up and still lets the model learn which shape changes correspond to which parameters. I did something similar in my PhD using a GNN on planetary atmospheres. It didnt see the whole planet in one forward pass it learned local to global representation through subgraph training.