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README.md

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+---
+license: apache-2.0
+#User-Defined Tags
+tags:
+  - CFDBench
+  - computational fluid dynamics
+  - neural operator
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">CFDBench</span>
+  </strong>
+</p>
+
+## Dataset Description
+
+CFDBench is a large-scale benchmark dataset for machine learning methods in computational fluid dynamics, designed to evaluate the generalization capabilities of neural operators under unseen boundary conditions, fluid properties, and geometries.
+
+The dataset contains four classic CFD problems: lid-driven cavity flow (cavity), laminar pipe flow (tube), step dam-break flow (dam), and flow around a cylinder (cylinder). For each problem, subsets are organized by variations in boundary conditions (`bc`), geometry (`geo`), and fluid properties (`prop`).
+
+Paper: [CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://arxiv.org/abs/2310.05963)
+
+## Supported Tasks
+
+| Scenario | Description |
+|---|---|
+| Flow field time-series prediction | Predict subsequent flow states from historical velocity fields. |
+| CFD surrogate modeling | Learn the mapping from physical parameters, boundary conditions, or geometry to velocity field evolution. |
+| Generalization evaluation | Evaluate model performance under unseen boundary conditions, fluid properties, and geometries. |
+| Neural operator research | Provide unified CFD evaluation data for models such as FNO and Transformer. |
+
+## Dataset Format and Structure
+
+The data is organized by problem type, subset, and case:
+
+```text
+data/<problem>/<subset>/case*/
+```
+
+Each case contains a physical parameter file and a two-dimensional velocity field time series:
+
+| File | Format | Description |
+|---|---|---|
+| `case.json` | JSON | Fluid properties, boundary conditions, or geometric parameters. |
+| `u.npy` | NumPy NPY | Horizontal velocity component with shape `[T, 64, 64]` and dtype `float64`. |
+| `v.npy` | NumPy NPY | Vertical velocity component with the same shape as `u.npy`. |
+
+Some cases also provide `u.png` and `v.png` as preview images; they are not required training data.
+
+## How to Use the Dataset
+
+This dataset is compatible with the `OneScience-Sugon/CFDBench` model. Download the dataset and model:
+
+```bash
+hf download --dataset OneScience-Sugon/cfdbench --local-dir ./data
+```
+## Official OneScience Information
+
+| Platform | OneScience Main Repository | Skills Repository |
+|---|---|---|
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## Citation and License
+
+- Luo, Y. et al. *CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics*, 2023.
+- This dataset is organized from the original CFDBench data and is licensed under the same Apache License 2.0.

README_zh.md

@@ -0,0 +1,70 @@
+---
+license: Apache License 2.0
+#用户自定义标签
+tags:
+  - CFDBench
+  - computational fluid dynamics
+  - neural operator
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">CFDBench</span>
+  </strong>
+</p>
+
+## 数据集描述
+
+CFDBench 是面向计算流体力学机器学习方法的大规模基准数据集，用于评估神经算子在未见边界条件、流体属性和几何形状下的泛化能力。
+
+数据集包含四类经典 CFD 问题：顶盖驱动方腔流（cavity）、圆管层流（tube）、阶梯溃坝流（dam）和圆柱绕流（cylinder）。每类问题按边界条件（`bc`）、几何形状（`geo`）和流体属性（`prop`）变化组织子集。
+
+论文：[CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://arxiv.org/abs/2310.05963)
+
+## 数据集支持的任务
+
+| 场景 | 说明 |
+|---|---|
+| 流场时序预测 | 根据历史速度场预测后续流动状态。 |
+| CFD 代理建模 | 学习物理参数、边界条件或几何到速度场演化的映射。 |
+| 泛化能力评测 | 评估模型在未见边界条件、物性和几何条件下的表现。 |
+| 神经算子研究 | 为 FNO、Transformer 等模型提供统一的 CFD 评测数据。 |
+
+## 数据集的格式和结构
+
+数据按问题类型、子集和 case 组织：
+
+```text
+data/<problem>/<subset>/case*/
+```
+
+每个 case 包含物理参数文件和二维速度场时间序列：
+
+| 文件 | 格式 | 说明 |
+|---|---|---|
+| `case.json` | JSON | 物性、边界条件或几何参数。 |
+| `u.npy` | NumPy NPY | 水平速度分量，形状为 `[T, 64, 64]`，类型为 `float64`。 |
+| `v.npy` | NumPy NPY | 竖直速度分量，形状与 `u.npy` 相同。 |
+
+部分 case 还提供 `u.png`、`v.png` 作为预览图，不属于必需训练数据。
+
+## 数据集使用方式
+
+本数据集适配 `OneScience-Sugon/CFDBench` 模型。下载数据集和模型：
+
+```bash
+hf download --dataset OneScience-Sugon/cfdbench --local-dir ./data
+```
+## OneScience 官方信息
+
+| 平台 | OneScience 主仓库 | Skills 仓库 |
+|---|---|---|
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## 引用与许可证
+
+- Luo, Y. et al. *CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics*, 2023.
+- 本数据集基于CFDBench原始数据进行整理，许可证同Apache License 2.0。

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