DeepCFD
# Model Overview DeepCFD is a U-Net-based surrogate model developed by the German Aerospace Center for two-dimensional steady-state laminar flows. It uses geometric information to rapidly predict velocity and pressure fields, accelerating the evaluation of channel flows, flows around obstacles, and aerodynamic designs. Paper: [DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks](https://arxiv.org/abs/2004.08826). # Model Description DeepCFD uses a U-Net deep convolutional architecture that takes a signed distance field (SDF) and flow-region mask as inputs to rapidly predict velocity and pressure fields for two-dimensional, nonuniform, steady-state laminar flows. ## Use Cases | Use Case | Description | | ---------- | ---------------------------------- | | Steady-state laminar flow prediction | Approximate velocity and pressure fields in two-dimensional, nonuniform, steady-state laminar flows | | Channel-flow simulation | Predict flow distributions around randomly shaped obstacles in a channel | | CFD surrogate modeling | Replace conventional CFD workflows such as OpenFOAM to improve inference efficiency | | Aerodynamic and fluid-shape optimization | Evaluate large numbers of candidate geometries for low-speed laminar-flow design | # 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/DeepCFD --local_dir ./DeepCFD cd DeepCFD ``` ### 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 `deepcfd` dataset for training. Download it with the command below and verify that the data path in `conf/config.yaml` is configured correctly: ```bash modelscope download --dataset OneScience/deepcfd --local_dir ./data ``` The project dataset can also be [downloaded from Zenodo](https://zenodo.org/record/3666056/files/DeepCFD.zip?download=1). The dataset contains two files: - `dataX.pkl`: Geometric input data for 981 channel-flow samples - `dataY.pkl`: Ground-truth CFD solutions for the corresponding samples, computed with the `simpleFOAM` solver ### Training Single GPU: - Training parameters are read from the `root.datapipe`, `root.model`, and `root.training` sections of `config/config.yaml`. The default configuration is intended for a minimal smoke test. - For full training, set `root.datapipe.source.data_dir` to the actual DeepCFD dataset directory and increase the model size, batch size, and number of epochs as needed. ```bash python scripts/train.py ``` Multiple GPUs: ```bash torchrun --standalone --nproc_per_node=