PaddleCFD is a deep learning toolkit for surrogate modeling, equation discovery, shape optimization and flow-control strategy discovery in the field of fluid mechanics. Currently, it mainly supports surrogate modeling, including models based on Fourier Neural Operator (FNO), Transformer, Diffusion Model (DM), Kolmogorov-Arnold Networks (KAN) and DeepONet.
doc: documentationexamples: example scriptsppcfd/data: data-process source codeppcfd/model: model source codeppcfd/utils: utils code
conda create --name ppcfd python=3.10
conda activate ppcfd
python -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
# Download and install paddle-backended Open3D
wget https://paddle-org.bj.bcebos.com/paddlecfd/envs/open3d-0.18.0+da239b25-cp310-cp310-manylinux_2_31_x86_64.whl
python -m pip install open3d-0.18.0+da239b25-cp310-cp310-manylinux_2_31_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple
# Unzip compiled customed operator (fused_segment_csr) to conda env directory
wget https://paddle-org.bj.bcebos.com/paddlecfd/envs/fused_segment_csr.tar.gz
tar -xzvf fused_segment_csr.tar.gz -C /root/miniconda3/envs/ppcfd/
# Add environment variable
export LD_LIBRARY_PATH=/root/miniconda3/envs/ppcfd/lib/python3.10/site-packages/paddle/libs:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/root/miniconda3/envs/ppcfd/lib/python3.10/site-packages/paddle/base:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/root/miniconda3/envs/ppcfd/lib:$LD_LIBRARY_PATH# Install PaddleCFD from sourcecode
python -m pip install -e . -i https://pypi.tuna.tsinghua.edu.cn/simple
# Install PaddleCFD from pypi
python -m pip install ppcfd -i https://pypi.tuna.tsinghua.edu.cn/simple# Run examples
cd PaddleCFD/examples/xxx/xxx
run the example according to the example README.mdPaddleCFD is provided under the Apache-2.0 license
