OneScience-Group/BearCFD-Ventilation

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

pip install -U huggingface_hub
hf download OneScience-Group/BearCFD-Ventilation --local-dir ./BearCFD-Ventilation
cd BearCFD-Ventilation

Install the runtime environment

DCU environment

# 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

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

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

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

python scripts/inference.py

Evaluation and visualization

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.