--- 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): ``` .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}, } ```