Neural Super-Resolution for Mesh-Based Simulations on Dataset 1
0.0226RMSESuperMeshNet
Evaluation Results
| Method | Links | |
|---|---|---|
| SuperMeshNetNh (number of HR data)=20, N (number of low-resolution data)=200, Inductive biases=with inductive biases, Supervision type=partial supervision, Base architecture=MGN2026.05 | 0.0226 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=MGN2026.05 | 0.0226 | |
| Fully supervisedNh (number of HR data)=200, N (number of low-resolution data)=200, Inductive biases=without inductive biases, Supervision type=full supervision, Base architecture=MGN2026.05 | 0.0228 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=MGN2026.05 | 0.0228 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=MGN2026.05 | 0.0269 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GIN2026.05 | 0.0277 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GIN2026.05 | 0.0381 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GTR2026.05 | 0.0385 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GIN2026.05 | 0.0404 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GCN2026.05 | 0.0431 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GTR2026.05 | 0.045 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=SAGE2026.05 | 0.045 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GTR2026.05 | 0.0451 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GAT2026.05 | 0.0457 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GAT2026.05 | 0.0512 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=SAGE2026.05 | 0.0544 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GAT2026.05 | 0.0544 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GCN2026.05 | 0.0575 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=SAGE2026.05 | 0.0589 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GCN2026.05 | 0.0613 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=MGN2026.05 | 0.0655 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GTR2026.05 | 0.0758 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GIN2026.05 | 0.0819 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GAT2026.05 | 0.0826 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GCN2026.05 | 0.0874 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=SAGE2026.05 | 0.0876 | |
| MAgNetNh (number of HR data)=0, N (number of low-resolution data)=200, Supervision type=no HR supervision (zero-shot)2026.05 | 0.0979 |