---
license: cc-by-nc-4.0
task_categories:
  - time-series-forecasting
tags:
  - physics
  - fluid-dynamics
  - pde
  - sim2real
  - piv
  - neurips-2026
pretty_name: RealPDE Competition Data (NeurIPS 2026)
size_categories:
  - 1K<n<10K
---

# RealPDE Competition Data (NeurIPS 2026)

Training data and baseline checkpoints for the [NeurIPS 2026 RealPDE
Competition](https://realpdecompetition.github.io/). This is a mirror of the
competition's Google Drive release, hosted here because the Drive link runs into
a per-file anonymous download quota when many people fetch it at once.

Both tracks share this release:

- **Track 1, Sim2Real** — [codabench.org/competitions/17363](https://www.codabench.org/competitions/17363/)
- **Track 2, LTTTA** — [codabench.org/competitions/17385](https://www.codabench.org/competitions/17385/)

## Contents

```text
train_sim.tar.gz              simulated training trajectories, for pretraining
train_real.tar.gz             real-world PIV training trajectories, for finetuning
example_data/3750_0.h5        one real trajectory, for checking shapes locally
baseline_checkpoints/
  pack_ckpt_fp16.py           complex-safe fp16 packing helper
  sim_pretrain/               CNO, FNO, FNO-fp16, Transolver, simulation only
  sim_real_ft/                the same four, finetuned on real data
```

The hidden evaluation set is **not** part of this release and is not published
anywhere. It exists only inside the Codabench evaluation container.

## Physical setting

Flow around a NACA4418 airfoil cross-section. Real measurements are
time-resolved Particle Image Velocimetry in a circulating tunnel; the simulated
split is matched 3D CFD under the same geometry and operating conditions.
Channels are `[u, v, p]`, and `p` is zero on real data because it is not
measured. Competition evaluation runs at `32 x 64` after 2x spatial
subsampling; the raw PIV fields are `64 x 128`.

`example_data/3750_0.h5` stores `u` and `v` at the top level with shape
`(T=868, 64, 128)`, plus scalars `aoa` and `re` and the grids `x`, `y`. Note the
training tarballs use a different layout, with `u` and `v` under
`measured_data/`.

## Baseline checkpoints

All were trained with the [RealPDEBench](https://github.com/AI4Science-WestlakeU/RealPDEBench)
code. Sizes: CNO 32 MB, Transolver 50 MB, FNO 403 MB in fp32 and 201 MB packed
to fp16. Hyper-parameters should be read from the `state_dict` shapes rather
than from the repository's yaml configs, which are not reliable for these
checkpoints; the competition starting kit documents them.

FNO spectral weights are `complex64`, so a naive `.half()` breaks them.
`pack_ckpt_fp16.py` stores them as `view_as_real(t).half()` with a
`complex_keys` list, and the starting kit's `load_baseline.unpack_fp16` reads
that format back.

## Using this in the competition

Competition rules require that training data come from this release. Model
weights trained from it are permitted, including the baseline checkpoints above.
Generating additional data, augmenting this release into extra training data,
and finetuning a model pretrained on anything else are not permitted, and the
rule is checked when the shortlisted teams are re-trained from scratch before
the final ranking. The Rules page of each track is authoritative.

## License

Released for non-commercial research and competition use under CC BY-NC 4.0. Do
not redistribute the hidden validation or test data, which is not in this
release in any case.

## Contact

realpde-competition@googlegroups.com, or the forum on either Codabench page.