PDE Solving on Poisson 2D (test)
0.0012MSEAD-FP-FR
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| AD-FP-FRPrecision=Full2025.12 | 0.0012 | 0.0025 | 0.0029 | — | — | |
| SE-FP-FRPrecision=Full2025.12 | 0.0016 | 0.0031 | 0.0039 | — | — | |
| PINTATT-rank=R = 322025.12 | 0.002 | 0.0038 | 0.0049 | — | — | |
| PINTATT-rank=R = 162025.12 | 0.0023 | 0.0044 | 0.0057 | — | — | |
| PINTATT-rank=R = 82025.12 | 0.0033 | 0.0068 | 0.0082 | — | — | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=112024.06 | 1.03 | — | — | — | 3.86 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=22024.06 | 1.18 | — | — | — | 4.18 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=142024.06 | 1.51 | — | — | — | 1.77 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=122024.06 | 1.6 | — | — | — | 1.12 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=122024.06 | 1.61 | — | — | — | 5.13 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=92024.06 | 1.69 | — | — | — | 6.66 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=52024.06 | 1.76 | — | — | — | 5.15 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=02024.06 | 1.77 | — | — | — | 3.38 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=52024.06 | 1.78 | — | — | — | 2.51 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=72024.06 | 1.97 | — | — | — | 5.34 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=112024.06 | 2.02 | — | — | — | 1.31 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=12024.06 | 2.18 | — | — | — | 2.08 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=42024.06 | 2.35 | — | — | — | 2.89 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=22024.06 | 2.38 | — | — | — | 1.55 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=102024.06 | 2.47 | — | — | — | 6.15 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=92024.06 | 2.57 | — | — | — | 4.1 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=02024.06 | 2.6 | — | — | — | 6.22 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=102024.06 | 2.72 | — | — | — | 3.88 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=152024.06 | 2.74 | — | — | — | 1.23 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=132024.06 | 2.86 | — | — | — | 4.39 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=32024.06 | 3.19 | — | — | — | 5.57 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=132024.06 | 3.85 | — | — | — | 1.07 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=72024.06 | 3.87 | — | — | — | 3.37 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=82024.06 | 4.04 | — | — | — | 9.83 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=122024.06 | 4.38 | — | — | — | 1.16 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=62024.06 | 4.92 | — | — | — | 2.61 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=02024.06 | 4.95 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=52024.06 | 4.96 | — | — | — | 1.94 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=82024.06 | 4.98 | — | — | — | 2.44 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=42024.06 | 4.99 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=92024.06 | 5 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=22024.06 | 5.01 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=72024.06 | 5.01 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=142024.06 | 5.01 | — | — | — | 1.95 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=102024.06 | 5.02 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=32024.06 | 5.03 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=112024.06 | 5.03 | — | — | — | 1.94 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=62024.06 | 5.04 | — | — | — | 1.94 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=42024.06 | 5.34 | — | — | — | 3.12 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=152024.06 | 6.58 | — | — | — | 6.25 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=32024.06 | 6.68 | — | — | — | 1.19 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=152024.06 | 6.85 | — | — | — | 7.97 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=142024.06 | 7.42 | — | — | — | 3.41 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=12024.06 | 7.95 | — | — | — | 3.35 | |
| FCNNArchitecture Size=(100, –, 6), #parameters (trained / all)=50901 / 50901, seed for randomness=82024.06 | 8.2 | — | — | — | 3.22 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=12024.06 | 9.32 | — | — | — | 6.76 | |
| MMNN1Architecture Size=(301, 16, 6), #parameters (trained / all)=24462 / 50950, seed for randomness=132024.06 | 9.35 | — | — | — | 1.52 | |
| MMNN2Architecture Size=(503, 20, 6), #parameters (trained / all)=50904 / 105228, seed for randomness=62024.06 | 9.68 | — | — | — | 4.65 | |
| AW-PINNNB=4000, NR=10000, Avg. Training Time=5.14 min2026.04 | — | — | 3.42 | — | — | |
| DeepONetParams=280K2026.02 | — | — | — | 0.02 | — | |
| FactFormerParams=3.9M2026.02 | — | — | — | 0.006 | — | |
| FNOParams=132K2026.02 | — | — | — | 0 | — | |
| Green LearningParams=83K2026.02 | — | — | — | 0.0001 | — | |
| MMPINNNB=6000, NR=30000, Avg. Training Time=7.17 min2026.04 | — | — | 5.71 | — | — | |
| Neural-HSSParams=37K2026.02 | — | — | — | 0 | — | |
| ResNetParams=165K2026.02 | — | — | — | 0.0003 | — | |
| W-PINNNB=4000, NR=10000, Avg. Training Time=4.87 min2026.04 | — | — | 3.55 | — | — |