Neural Super-Resolution for Mesh-Based Simulations on Dataset 2
0.0461RMSEFully supervised
Evaluation Results
| Method | Links | |
|---|---|---|
| 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 | 0.0461 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=MGN2026.05 | 0.0461 | |
| 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 | 0.0507 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=MGN2026.05 | 0.0507 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=MGN2026.05 | 0.0514 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GIN2026.05 | 0.0534 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GIN2026.05 | 0.0537 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GIN2026.05 | 0.0569 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GTR2026.05 | 0.0572 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GCN2026.05 | 0.0574 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GTR2026.05 | 0.06 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GCN2026.05 | 0.0624 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=SAGE2026.05 | 0.0624 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GTR2026.05 | 0.0631 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=SAGE2026.05 | 0.0633 | |
| SuperMeshNetNh, N=20, 200, MPNN Architecture=GAT2026.05 | 0.0634 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GCN2026.05 | 0.0636 | |
| Fully supervisedNh, N=200, 200, MPNN Architecture=GAT2026.05 | 0.0637 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=SAGE2026.05 | 0.0664 | |
| SuperMeshNet-ONh, N=20, 200, MPNN Architecture=GAT2026.05 | 0.068 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=MGN2026.05 | 0.073 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GIN2026.05 | 0.0775 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GCN2026.05 | 0.0972 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GAT2026.05 | 0.0983 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=GTR2026.05 | 0.0983 | |
| Fully supervisedNh, N=20, 20, MPNN Architecture=SAGE2026.05 | 0.1025 | |
| MAgNetNh (number of HR data)=0, N (number of low-resolution data)=200, Supervision type=no HR supervision (zero-shot)2026.05 | 0.1305 |