PDE Solving on Poisson 1d (test)
0Relative MSENeural Solver
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Neural SolverTraining Paradigm=Hybrid2024.10 | 0 | — | — | |
| Neural SolverLearning Paradigm=Neural Solver2024.10 | 0.0001 | — | — | |
| PINOLearning Paradigm=Hybrid2024.10 | 0.0028 | — | — | |
| PINOTraining Paradigm=Hybrid2024.10 | 0.0033 | — | — | |
| PPINNsTraining Paradigm=Unsupervised2024.10 | 0.0359 | — | — | |
| PPINNsLearning Paradigm=Unsupervised2024.10 | 0.043 | — | — | |
| PINNS-multi-optLearning Paradigm=Unsupervised2024.10 | 0.118 | — | — | |
| PI-DeepONetLearning Paradigm=Hybrid2024.10 | 0.12 | — | — | |
| PI-DeepONetTraining Paradigm=Hybrid2024.10 | 0.125 | — | — | |
| PO-DeepONetLearning Paradigm=Unsupervised2024.10 | 0.143 | — | — | |
| MLP + basisLearning Paradigm=Supervised2024.10 | 0.15 | — | — | |
| P2INNsLearning Paradigm=Unsupervised2024.10 | 0.15 | — | — | |
| MLP + basisTraining Paradigm=Supervised2024.10 | 0.158 | — | — | |
| PO-DeepONetTraining Paradigm=Unsupervised2024.10 | 0.179 | — | — | |
| PINNs+L-BFGSLearning Paradigm=Unsupervised2024.10 | 0.883 | — | — | |
| Baldwinian-PINNTest task parameters=α1 = 1, α2 = 1, α3 = 0.1, α4 = 0, ω1 = 0.7, ω2 = 1.5, x ∈ [−10, 10], Training task distribution=60 train tasks from α's∈ [0, 4], ω's∈ [0, 4]2023.12 | — | 0 | 0 | |
| NRPINNFine-tuning iterations=900, Test task parameters=α1 = 1, α2 = 1, α3 = 0.1, α4 = 0, ω1 = 0.7, ω2 = 1.5, x ∈ [−10, 10], Training task distribution=60 train tasks from α1 = 1, α2 = 1, α3 = 0.1, α4 = 0, ω1 ∈ [0, 1], ω2 ∈ [0.2]2023.12 | — | 0.0005 | — |