| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - BENO |
| - elliptic PDEs |
| - boundary-embedded neural operator |
| frameworks: PyTorch |
| --- |
| |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">BENO</span> |
| </strong> |
| </p> |
| |
| # Model Overview |
|
|
| BENO is a boundary-embedded neural operator developed by researchers at Peking University for solving elliptic PDEs subject to complex boundary conditions. It enables rapid prediction of steady-state physical fields across varying geometries and nonhomogeneous boundary conditions. |
|
|
| Paper: [BENO: Boundary-embedded Neural Operators for Elliptic PDEs](https://openreview.net/forum?id=ZZTkLDRmkg) |
|
|
| # Model Description |
| BENO employs a boundary-embedded neural operator architecture that combines a dual-branch graph neural network with a Transformer. Trained on the BENO dataset, it predicts steady-state physical fields governed by elliptic PDEs such as the Poisson and Laplace equations on complex geometries with nonhomogeneous boundary conditions. |
|
|
| # Use Cases |
|
|
| | Use Case | Description | |
| | ---------- | --------------------------------- | |
| | Elliptic PDE solving | Rapidly approximate steady-state boundary-value problems such as the Poisson and Laplace equations | |
| | Complex boundary modeling | Capture the effects of free-form boundaries, irregular domains, and nonhomogeneous boundary values on the solution field | |
| | Steady-state field prediction | Predict equilibrium physical fields determined jointly by source terms and boundary conditions | |
| | Numerical solver acceleration | Replace or augment conventional FEM, FDM, and FVM workflows to improve inference efficiency | |
|
|
| # Usage |
| ## 1. OneCode |
|
|
| Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience: |
|
|
| [Launch OneCode for one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
|
| ## 2. Manual Setup |
|
|
| **Hardware Requirements** |
|
|
| - A GPU or DCU is recommended. |
| - A CPU can be used for import checks and small-scale pipeline validation, but full training and inference will be slow. |
| - DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster, is recommended. |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| modelscope download --model OneScience/BENO --local_dir ./BENO |
| cd BENO |
| ``` |
|
|
| ### Set Up the Runtime Environment |
|
|
|
|
| **DCU Environment** |
|
|
| ```bash |
| # Activate DTK and Conda first |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| # Installation with uv is also supported |
| pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| **GPU Environment** |
| ```bash |
| # Activate Conda first |
| conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 |
| conda activate onescience311 |
| # Installation with uv is also supported |
| pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| ### Training Data |
| The OneScience community provides the `beno` dataset for training. Download it with the command below and verify that the data path in `config/config.yaml` is configured correctly: |
|
|
| ```bash |
| modelscope download --dataset OneScience/beno --local_dir ./data |
| ``` |
| The complete dataset is also available from the [official download link](https://drive.google.com/file/d/11PbUrzJ-b18VhFGY_uICSciCkeGrsaTZ/view). |
|
|
| ### Training |
|
|
| Single GPU: |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| Multiple GPUs: |
| ``` |
| torchrun --standalone --nnodes=<num_nodes> --nproc_per_node=<num_GPUs> scripts/train.py |
| ``` |
|
|
| ### Model Weights |
| This repository will provide weights trained on the BENO dataset in the `weights/` directory. The weights will be uploaded soon. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| # Official OneScience Resources |
|
|
| | Platform | OneScience 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 | |
|
|
| # Citations and License |
|
|
| - Original BENO paper: [BENO: Boundary-embedded Neural Operators for Elliptic PDEs](https://proceedings.iclr.cc/paper_files/paper/2024/file/218ca0d92e6ed8f9db00621e103dc70c-Paper-Conference.pdf) |
| - This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope. Before public redistribution, verify the applicable licensing requirements of the upstream project. |
|
|
