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OneScience-Group/BearCFD-Ventilation

sourceHugging Faceapache-2.0updated 9d agoView on Hugging Face

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Model Card

BearCFD-Ventilation

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

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

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.
—Dataset: 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.