TurtLES: A Large-Scale Benchmark for Turbulent 3D Neural PDE Surrogates

Paper: arXiv:2611.09567 · Project page: turtlesprojectml-alt.github.io

Dataset details

Case structure

mini-TurtLES

Download instructions

We provide download_turtles.py, which can be used to download the full dataset, any mini-TurtLES subset, or single cases.

The downloaded data keeps the repository layout (data/<split>/<case_id>/). Files that are already present are skipped. Moving up a tier (e.g. from TurtLES-G1T100 to TurtLES-G1T200) therefore only fetches the difference. A download interrupted with Ctrl+C or a lost connection continues where it stopped when you run the same command again. Unfinished files never appear in data/.

Installation

pip install -U huggingface_hub
hf auth login    # Downloads also work without logging in, but logged-in downloads get higher rate limits.
hf download turtlesprojectml/turtles download_turtles.py --repo-type dataset --local-dir .           

Usage

python download_turtles.py --list                       # lists all subsets with their exact sizes
python download_turtles.py TurtLES-debug                # downloads 100 snapshots of the reference case
python download_turtles.py TurtLES-G1T100               # downloads the mini-TurtLES subset G1T100
python download_turtles.py TurtLES-full                 # downloads the full dataset (14 TB)
python download_turtles.py Re1089_alpha12_a1p302_n2     # downloads a single case (all 400 frames)

Option	Effect
--dir PATH	Specify a PATH to download to (default: current directory)
--split test	Download only the specified splits, e.g. --split test or --split train,val
--with-vtu	Also download each case's *_template.vtu
--yes	Start without asking for confirmation (for batch jobs)

Code and baselines

We provide a GitHub repository that includes baseline implementations and evaluation scripts, to facilitate training of neural surrogates on TurtLES.

Citation

If you find our work useful or use our dataset, please consider citing it:

@inproceedings{platenburg2026turtles,
  title     = {{TurtLES}: A Large-Scale Benchmark for Turbulent {3D} Neural {PDE} Surrogates},
  author    = {Platenburg, Jarno and Kassa{\"\i} Koupa{\"\i}, Armand and Cinnella, Paola and Gallinari, Patrick},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026},
  url       = {https://arxiv.org/abs/2611.09567}
}