TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning
Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbulence has fundamentally different physics and is far more costly to simulate. Existing 3D resources also typically provide only one realization per configuration, making it difficult to distinguish learning the dynamics from fitting the statistics of a single flow. In this paper, we introduce TIDE (Turbulent Incompressible DNS Ensembles), a 256^3 DNS corpus and benchmark for 3D incompressible turbulence, with 15 configurations on eight controlled axes, independent ensembles, pressure fields, and equation-level ve...
| Authors | Yilong Dai, Yiming Sun, Yiheng Chen et al. |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
Models and datasets referencing this paper
- ydai17/TIDE-ou_relam90_256_fp64Dataset
- kenalin/TIDE-rotating_ro0p2_256_fp64Dataset
- ydai17/TIDE-ou_relam50_256_fp64Dataset
- ydai17/TIDE-ou_relam70_256_fp64Dataset
- ydai17/TIDE-decay_saffman_v2Dataset
- ydai17/TIDE-rotating_ro0p2_v2_256_fp64Dataset
- ydai17/TIDE-ou_robust_kf4_256_fp64Dataset
- ydai17/TIDE-scalar_sc1_256_fp64Dataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.