--- frameworks: - "" language: - en license: apache-2.0 tags: - OneScience - fluid dynamics - neural PDE solver evaluation - unstructured-mesh simulation - multiphysics modeling ---

CFD_Benchmark

# Model Overview CFD_Benchmark is an open-source deep-learning benchmark library for research on neural partial differential equation (PDE) solvers. It extends Tsinghua University's open-source Neural-Solver-Library with Distributed Data Parallel (DDP) training support, additional models, and new datasets, while retaining the original neural-operator and physical-field modeling framework. The library supports neural PDE solver evaluation, deep-learning research for CFD, multi-model performance comparisons, large-scale distributed training experiments, physical simulation dataset development, and algorithm benchmarking. The library currently supports the following benchmarks: - Six standard benchmarks from [[FNO]](https://arxiv.org/abs/2010.08895) and [[geo-FNO]](https://arxiv.org/abs/2207.05209) - PDEBench [[NeurIPS 2022 Track dataset and benchmark]](https://arxiv.org/abs/2210.07182) for autoregressive tasks - The ShapeNet-Car dataset [[TOG 2018]](https://dl.acm.org/doi/abs/10.1145/3197517.3201325) for industrial design benchmarks - The BubbleML [[Multiphase Multiphysics Dataset]](https://arxiv.org/abs/2307.14623) for studying multiphysics phase-transition phenomena --- ## Supported Neural Solvers The following neural PDE solvers are supported: - **Transolver** - Transolver: A Fast Transformer Solver for PDEs on General Geometries [[ICML 2024]](https://arxiv.org/abs/2402.02366) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Transolver.py) - **ONO** - Improved Operator Learning by Orthogonal Attention [[ICML 2024]](https://arxiv.org/abs/2310.12487v3) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/ONO.py) - **Factformer** - Scalable Transformer for PDE Surrogate Modeling [[NeurIPS 2023]](https://arxiv.org/abs/2305.17560) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Factformer.py) - **U-NO** - U-NO: U-shaped Neural Operators [[TMLR 2023]](https://openreview.net/pdf?id=j3oQF9coJd) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/U_NO.py) - **LSM** - Solving High-Dimensional PDEs with Latent Spectral Models [[ICML 2023]](https://arxiv.org/pdf/2301.12664) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/LSM.py) - **GNOT** - GNOT: A General Neural Operator Transformer for Operator Learning [[ICML 2023]](https://arxiv.org/abs/2302.14376) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/GNOT.py) - **F-FNO** - Factorized Fourier Neural Operators [[ICLR 2023]](https://arxiv.org/abs/2111.13802) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/F_FNO.py) - **U-FNO** - An enhanced Fourier neural operator-based deep-learning model for multiphase flow [[Advances in Water Resources 2022]](https://www.sciencedirect.com/science/article/pii/S0309170822000562) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/U_FNO.py) - **Galerkin Transformer** - Choose a Transformer: Fourier or Galerkin [[NeurIPS 2021]](https://arxiv.org/abs/2105.14995) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Galerkin_Transformer.py) - **MWT** - Multiwavelet-based Operator Learning for Differential Equations [[NeurIPS 2021]](https://openreview.net/forum?id=LZDiWaC9CGL) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/MWT.py) - **FNO** - Fourier Neural Operator for Parametric Partial Differential Equations [[ICLR 2021]](https://arxiv.org/pdf/2010.08895) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/FNO.py) - **Transformer** - Attention Is All You Need [[NeurIPS 2017]](https://arxiv.org/pdf/1706.03762) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Transformer.py) - **GFNO** - Group Equivariant Fourier Neural Operators for Partial Differential Equations[[2023 Poster]](https://arxiv.org/pdf/1706.03762)[[Code]](https://github.com/divelab/AIRS/blob/main/OpenPDE/G-FNO/models/GFNO.py) Several vision architectures also serve as effective baselines for structured-geometry tasks: - **Swin Transformer** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [[ICCV 2021]](https://arxiv.org/abs/2103.14030) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Swin_Transformer.py) - **U-Net** - U-Net: Convolutional Networks for Biomedical Image Segmentation [[MICCAI 2015]](https://arxiv.org/pdf/1505.04597) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/U_Net.py) Several established geometric deep-learning models are included for design tasks: - **Graph-UNet** - Graph U-Nets [[ICML 2019]](https://arxiv.org/pdf/1905.05178) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Graph_UNet.py) - **GraphSAGE** - Inductive Representation Learning on Large Graphs [[NeurIPS 2017]](https://arxiv.org/pdf/1706.02216) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/GraphSAGE.py) - **PointNet** - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [[CVPR 2017]](https://arxiv.org/pdf/1612.00593) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/PointNet.py) The library also includes the following graph neural network: - **MeshGraphNet** LEARNING MESH-BASED SIMULATION WITH GRAPH NETWORKS[ICLR 2021](https://arxiv.org/abs/2010.03409) [[Code]](https://github.com/google-deepmind/deepmind-research/tree/master/meshgraphnets) ## Use Cases | Use Case | Description | |---|---| | Neural PDE solver evaluation | Train, run inference with, and compare models such as FNO, Transolver, GNOT, ONO, and U-NO through a unified workflow | | Autoregressive physical prediction | Predict the temporal evolution of PDE states step by step using datasets such as PDEBench | | Multiphysics modeling | Study multiphase flows, multiphysics coupling, and phase-transition phenomena using datasets such as BubbleML | | ModelScope/OneCode execution | Download the standalone model package, install its dependencies, and run the provided scripts | # 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/CFD_Benchmark --local_dir ./CFD_Benchmark cd CFD_Benchmark ``` ### 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 Use the dataset links in the benchmark overview above to download the required data. The six standard benchmark datasets from [[FNO]](https://arxiv.org/abs/2010.08895) and [[geo-FNO]](https://arxiv.org/abs/2207.05209) are available from [this link](https://drive.google.com/drive/folders/1YBuaoTdOSr_qzaow-G-iwvbUI7fiUzu8). The PDEBench [[NeurIPS 2022 Track dataset and benchmark]](https://arxiv.org/abs/2210.07182), used for benchmarking autoregressive tasks, is available from [this link](https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986). The ShapeNet-Car [[TOG 2018]](https://dl.acm.org/doi/abs/10.1145/3197517.3201325) benchmark dataset for industrial design tasks is available from [[this link]](http://www.nobuyuki-umetani.com/publication/mlcfd_data.zip). The BubbleML [[Multiphase Multiphysics Dataset]](https://arxiv.org/abs/2307.14623), designed for research on multiphysics phase-transition phenomena, is available from [[this link]](https://github.com/HPCForge/BubbleML/blob/main/bubbleml_data/README.md). The OneScience community also provides the `cfd_benchmark` dataset for training. Download it with the command below and verify that the data path in `conf/config.yaml` is configured correctly: ``` modelscope download --dataset OneScience/cfd_benchmark --local_dir ./data ``` ### Training ```bash python scripts/train.py ``` ### Model Weights This repository will provide weights trained on the OneScience `cfd_benchmark` dataset in the `weights/` directory. The weights will be uploaded soon. ### Inference ```bash python scripts/inference.py ``` Inference loads the trained weights referenced by `paths.weight_path` and writes the metrics to: ```text ./results/{train.save_name}/metrics.json ``` # Official OneScience Resources | Platform | OneScience Repository | Skills 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 | # Citations and License - Reference repository: [Neural-Solver-Library](https://github.com/thuml/Neural-Solver-Library). - This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope.