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license: cc-by-4.0
task_categories:
- time-series-forecasting
- image-to-image
tags:
- turbulence
- computational-fluid-dynamics
- direct-numerical-simulation
- navier-stokes
- neural-operator
- scientific-machine-learning
- physics
arxiv: 2608.04222
pretty_name: 'TIDE: Turbulent Incompressible DNS Ensembles'
size_categories:
- n>1T
---
# TIDE: Turbulent Incompressible DNS Ensembles
A physically diverse 3D turbulence corpus: **15 configurations** of the same
incompressible Navier–Stokes system along **eight physics axes**, each shipping
**8–16 fully independent realizations** at **256³ in fp64** (134 trajectories,
~2.6 TB), released only after passing a fixed acceptance standard of
statistical gates and equation-level residual checks.
- **Code (solver, acceptance referee, benchmark):** https://github.com/Dyloong1/TIDE-dataset-benchmark
- **Citable DOI record (datasheet, code snapshot, split manifest):** https://doi.org/10.5281/zenodo.21589489
- **Paper:** [arXiv:2608.04222](https://arxiv.org/abs/2608.04222) — *TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning* (under submission, ACM SIGKDD Datasets & Benchmarks Track)
> **All 15 configurations are live.** Each ships as its own repository (linked
> in the table below). This index repository carries the datasheet, the split
> manifest, and the per-configuration manifests.
## Structure
TIDE is one incompressible NS system: one solver, one grid, one acceptance
standard, and only the physics varies.
| Family | What is done to the dynamics | Axes |
|---|---|---|
| **Forced isotropic** | equations untouched, flow held statistically steady by stochastic injection | Reynolds number, forcing scale, forcing memory, helicity |
| **Extended physics** | exactly one ingredient added (a term in the momentum balance or a transported field) | rotation, stratification, passive scalar |
| **Free decay** | the drive removed | initial state (four controlled families) |
## Configurations
Each configuration lives in its own repository (so you can download exactly what
you need). Sizes are the on-disk zarr size.
| Configuration | Repository | Size | Family |
|---|---|---|---|
| Re_lambda 86 (flagship) | [`ydai17/TIDE-ou_relam90_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-ou_relam90_256_fp64) | 478 GB | forced |
| k_f = 3 | [`ydai17/TIDE-ou_robust_kf3_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-ou_robust_kf3_256_fp64) | 237 GB | forced |
| k_f = 4 | [`ydai17/TIDE-ou_robust_kf4_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-ou_robust_kf4_256_fp64) | 238 GB | forced |
| tau = 1 | [`ydai17/TIDE-ou_robust_tau1_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-ou_robust_tau1_256_fp64) | 295 GB | forced |
| rotating (strong) | [`ydai17/TIDE-rotating_ro0p2_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-rotating_ro0p2_256_fp64) | 222 GB | extended |
| rotating (moderate) | [`ydai17/TIDE-rotating_ro0p2_v2_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-rotating_ro0p2_v2_256_fp64) | 231 GB | extended |
| passive scalar | [`ydai17/TIDE-scalar_sc1_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-scalar_sc1_256_fp64) | 269 GB | extended |
| decay (hot-start) | [`ydai17/TIDE-decay_hotstart_re86`](https://huggingface.co/datasets/ydai17/TIDE-decay_hotstart_re86) | 80 GB | decay |
| decay (Saffman) | [`ydai17/TIDE-decay_saffman_v2`](https://huggingface.co/datasets/ydai17/TIDE-decay_saffman_v2) | 79 GB | decay |
| decay (Batchelor) | [`ydai17/TIDE-decay_batchelor_v2`](https://huggingface.co/datasets/ydai17/TIDE-decay_batchelor_v2) | 80 GB | decay |
| Re_lambda 70 | [`ydai17/TIDE-ou_relam70_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-ou_relam70_256_fp64) | 473 GB | forced |
| Re_lambda 55 | [`ydai17/TIDE-ou_relam50_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-ou_relam50_256_fp64) | 320 GB | forced |
| helical | [`ydai17/TIDE-helical_re86_retune2_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-helical_re86_retune2_256_fp64) | 236 GB | forced |
| stratified | [`ydai17/TIDE-stratified_reb40_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-stratified_reb40_256_fp64) | 294 GB | extended |
| decay (ABC) | [`ydai17/TIDE-abc_turb_full_256_fp64`](https://huggingface.co/datasets/ydai17/TIDE-abc_turb_full_256_fp64) | 94 GB | decay |
## What a frame contains
Each configuration is one chunked zarr store, one frame per chunk (zstd):
```
<CASE>.zarr/
u [N, 3, 256, 256, 256] fp32 velocity
p [N, 256, 256, 256] fp32 pressure (spectrally solved, certified)
theta / b [N, 256, 256, 256] fp32 passive scalar / buoyancy (5-channel cases)
t [N] physical time of each frame
k_max_eta [N] per-frame resolution margin
seed [N] trajectory index
```
Fields are computed in fp64 and stored in fp32; frames are exported every
0.05 T_L (about one Kolmogorov time). Every released frame is individually
Class I (k_max·eta >= 1.5).
## Usage
```bash
pip install -U huggingface_hub zarr
huggingface-cli download ydai17/TIDE-ou_relam90_256_fp64 --repo-type dataset \
--local-dir ./tide-data/corpus
```
```python
import zarr
z = zarr.open("./tide-data/corpus/ou_relam90_256_fp64.zarr", mode="r")
u = z["u"][0] # (3, 256, 256, 256) velocity of the first frame
print(z["t"][:5], z["k_max_eta"][:5])
```
The benchmark harness reads these stores directly; see the code repository for
the training and evaluation protocol, the acceptance referee, and the released
result rows behind every number in the paper.
## Files in this index repository
- `DATASHEET.md` — datasheet for the dataset
- `benchmark_slice.json` — the deterministic train/val/test manifest (3 train,
1 validation, 3 test trajectories per configuration, chosen by a
model-independent quality score)
- `manifests/` — per-configuration manifests (seed and frame counts, channels)
## License and citation
Data under CC-BY-4.0. Please cite the paper (see the code repository's
`CITATION.cff`) and the DOI record above.
## Citation
```bibtex
@misc{dai2026tide,
title={TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning},
author={Yilong Dai and Yiming Sun and Yiheng Chen and Shengyu Chen and Peyman Givi and Xiaowei Jia and Runlong Yu},
year={2026},
eprint={2608.04222},
archivePrefix={arXiv},
primaryClass={physics.flu-dyn},
url={https://arxiv.org/abs/2608.04222},
}
```
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