How PDEForge Compares
An honest feature comparison with the main alternatives, as of mid-2026. Corrections welcome — open an issue.
| PDEForge | APEBench/exponax | PDEBench | The Well | PDEArena | py-pde | |
|---|---|---|---|---|---|---|
| Delivery | generate on demand | generate on demand | download (+per-PDE scripts) | download (15 TB) | download | DIY solver |
| One-call multi-physics API | yes | yes (scenarios) | no | no | no | no |
| Any resolution | yes | yes | regeneration is DIY | no | no | yes (DIY) |
| Any parameters | yes | yes | DIY | no | partial | yes (DIY) |
| Train/val/calibration/test | yes — native | no | no | no (3-way) | no (3-way) | no |
| OOD splits by parameter range | yes | no | no | no | no | no |
| Multi-fidelity pairs | yes | no | no | no | no | no |
| FEM / complex geometry | yes (FEniCSx) | no (periodic only) | no | fixed datasets | no | no |
| Stochastic PDEs | yes | no | no | some (fixed) | no | yes |
| Framework-agnostic output | NumPy/HDF5/zarr | JAX arrays | HDF5 | HDF5/torch | HDF5 | NumPy |
| GPU acceleration | optional JAX backend | JAX-native | n/a | n/a | n/a | numba |
| Base install | NumPy/SciPy (pip) | JAX (pip) | heavy | loader only | medium | pip |
| Convergence-verified data | yes (pdeforge.verify) |
no | no | no | no | no |
| PDE count | 30+ (and growing) | ~46 | 11 families | 16 datasets | 5 families | user-defined |
When to use something else. Pretraining a foundation model on tens of terabytes of heterogeneous physics: The Well. Benchmarking autoregressive emulator rollouts with differentiable-solver training in JAX: APEBench. Comparing against the literature's fixed reference numbers: PDEBench's published datasets. A PDE we don't implement and you want to hand-discretise: py-pde, Dedalus, or PhiFlow.
When to use PDEForge. Controlled studies that need data at YOUR
resolution and YOUR parameters; anything involving calibrated uncertainty
(conformal prediction needs the calibration split we ship natively);
distribution-shift and multi-fidelity experiments; complex-geometry flow
data without writing FEM code; reproducibility — every dataset regenerates
from its own metadata (pdeforge.reproduce).