| --- |
| license: other |
| |
| tags: |
| - BENO |
| - CFD |
| - neural operator |
| language: |
| - en |
| - zh |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">BENO</span> |
| </strong> |
| </p> |
| |
| ## Dataset Description |
|
|
| The BENO dataset originates from the ICLR 2024 paper *BENO: Boundary-Embedded Neural Operators for Elliptic PDEs* and is designed for solving elliptic partial differential equations under complex boundary conditions. The data contains random boundary geometries with four, three, two, one, or no corners, all standardized to a `32 x 32` grid resolution. |
|
|
| Paper: [BENO: Boundary-Embedded Neural Operators for Elliptic PDEs](https://proceedings.iclr.cc/paper_files/paper/2024/file/218ca0d92e6ed8f9db00621e103dc70c-Paper-Conference.pdf) |
|
|
| ## Supported Tasks |
|
|
| | Scenario | Description | |
| |---|---| |
| | Elliptic PDE solving | Predict the solution field from the boundary conditions and the right-hand side of the equation. | |
| | Boundary-condition research | Compare solution performance under Dirichlet and Neumann boundary conditions. | |
| | Geometry generalization evaluation | Evaluate model generalization across different random boundary shapes. | |
| | Neural operator research | Provide standardized training and evaluation data for operator models such as BENO. | |
|
|
| ## Dataset Format and Structure |
|
|
| The data is organized by boundary condition: |
|
|
| ```text |
| data/ |
| Dirichlet/ |
| Neumann/ |
| ``` |
|
|
| Each boundary-condition category contains the following six configurations: `N32_0c`, `N32_1c`, `N32_2c`, `N32_3c`, `N32_4c`, and `N32_mix`. Each configuration contains 1,000 `float64` samples: |
|
|
| | File | shape | Description | |
| |---|---|---| |
| | `BC_<prefix>_all.npy` | `[1000, 128, 4]` | Boundary coordinates, boundary values, and boundary features. | |
| | `RHS_<prefix>_all.npy` | `[1000, 1024, 4]` | Coordinates, source terms, and cell states on a 32×32 grid. | |
| | `SOL_<prefix>_all.npy` | `[1000, 1024, 1]` | Elliptic PDE solution fields. | |
|
|
| ## How to Use the Dataset |
|
|
| This dataset has been adapted for the `OneScience-Group/BENO` model. Download the dataset and model: |
|
|
| ```bash |
| hf download --dataset OneScience-Group/beno --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 |
|
|
| - Original BENO paper: [BENO: Boundary-embedded Neural Operators for Elliptic PDEs](https://openreview.net/forum?id=ZZTkLDRmkg) |
| - This dataset has been organized and converted from the original BENO dataset. Its use must comply with the licensing requirements published by the original project. |
|
|
