| --- |
| license: apache-2.0 |
| language: |
| - en |
| library_name: pytorch |
| tags: |
| - OneScience |
| - fluid-dynamics |
| - indoor-ventilation |
| - neural-operator |
| datasets: |
| - alwaysbyx/Bear-CFD-dataset |
| --- |
| |
| <p align="center"> |
| <strong><span style="font-size: 30px;">BearCFD-Ventilation</span></strong> |
| </p> |
|
|
| # Model Introduction |
|
|
| BearCFD-Ventilation predicts indoor CO2 concentration from the BEAR-CFD dataset. Given historical CO2 distributions and ventilation-control parameters, it forecasts how the CO2 concentration evolves. |
|
|
| Paper: [Building Control CFD: Efficient Deep Learning of Indoor CO2 Dynamics from Sustainable CFD Simulations](https://arxiv.org/pdf/2504.21243) |
|
|
| This repository is an independent OneScience reproduction of the BearCFD-Ventilation experiment using the paper description, official configuration, and Bear-CFD dataset. |
|
|
| # Model Description |
|
|
| BearCFD-Ventilation uses a neural-operator Transformer architecture for transient indoor-ventilation data and performs multistep CO2 forecasting in occupied regions. |
|
|
| ## Intended Uses |
|
|
| | Use case | Description | |
| | :--- | :--- | |
| | Indoor-ventilation prediction | Predict indoor CO2 concentration from supply-air velocity, supply-air angle, and occupancy. | |
| | CFD acceleration | Build a fast surrogate for transient indoor-ventilation CFD simulations. | |
|
|
| # Usage |
|
|
| ## 1. OneCode |
|
|
| Try one-click AI-for-Science programming in the OneCode online environment: |
|
|
| [Launch OneCode](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 imports and small connectivity checks, but full training and inference will be slow. |
| - DCU users should install DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster. |
|
|
| ### Download the model repository from Hugging Face |
|
|
| ```bash |
| pip install -U huggingface_hub |
| hf download OneScience-Group/BearCFD-Ventilation --local-dir ./BearCFD-Ventilation |
| cd BearCFD-Ventilation |
| ``` |
|
|
| ### Install the runtime environment |
|
|
| **DCU environment** |
|
|
| ```bash |
| # Activate DTK first. |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| **GPU environment** |
|
|
| ```bash |
| 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 |
| pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| ### Download the training dataset from Hugging Face |
|
|
| ```bash |
| hf download alwaysbyx/Bear-CFD-dataset --repo-type dataset --local-dir ./data |
| ``` |
|
|
| The directory contains `unsteady_10.pkl` through `unsteady_41.pkl`. Each sample includes occupied-zone CO2 concentration, inlet velocity, inlet angle, occupancy, and related fields. Before training, verify the data path in `config/config.yaml`. |
|
|
| ### Train |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| The best checkpoint is saved to `weight/best_model.pth`. |
|
|
| ### Pretrained weights |
|
|
| The repository includes `weight/best_model.pth`, trained on BEAR-CFD data and ready for inference. |
|
|
| The recorded test results are `relative_l2=0.144541` and `rmse=94.616395`. Table 3 of the paper reports an ensemble-test `l2 error` of 10.90%; the values here are from a run on 32 original official samples. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| ### Evaluation and visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| # OneScience |
|
|
| | Platform | OneScience repository | OneSkills 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 |
|
|
| - Paper: [Building Control CFD: Efficient Deep Learning of Indoor CO2 Dynamics from Sustainable CFD Simulations](https://arxiv.org/pdf/2504.21243). |
| - Dataset: [BEAR-CFD dataset](https://huggingface.co/datasets/alwaysbyx/Bear-CFD-dataset). |
| - This model repository uses the Apache License 2.0 identifier (`apache-2.0`) required by Hugging Face metadata. The dataset and other third-party assets remain subject to their original licenses and terms. |
|
|
