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] and [geo-FNO]
PDEBench [NeurIPS 2022 Track dataset and benchmark] for autoregressive tasks
The ShapeNet-Car dataset [TOG 2018] for industrial design benchmarks
The BubbleML [Multiphase Multiphysics Dataset] 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] [Code]
ONO - Improved Operator Learning by Orthogonal Attention [ICML 2024] [Code]
Factformer - Scalable Transformer for PDE Surrogate Modeling [NeurIPS 2023] [Code]
U-NO - U-NO: U-shaped Neural Operators [TMLR 2023] [Code]
LSM - Solving High-Dimensional PDEs with Latent Spectral Models [ICML 2023] [Code]
GNOT - GNOT: A General Neural Operator Transformer for Operator Learning [ICML 2023] [Code]
F-FNO - Factorized Fourier Neural Operators [ICLR 2023] [Code]
U-FNO - An enhanced Fourier neural operator-based deep-learning model for multiphase flow [Advances in Water Resources 2022] [Code]
Galerkin Transformer - Choose a Transformer: Fourier or Galerkin [NeurIPS 2021] [Code]
MWT - Multiwavelet-based Operator Learning for Differential Equations [NeurIPS 2021] [Code]
FNO - Fourier Neural Operator for Parametric Partial Differential Equations [ICLR 2021] [Code]
Transformer - Attention Is All You Need [NeurIPS 2017] [Code]
GFNO - Group Equivariant Fourier Neural Operators for Partial Differential Equations[2023 Poster][Code]

Several vision architectures also serve as effective baselines for structured-geometry tasks:

Swin Transformer - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [ICCV 2021] [Code]
U-Net - U-Net: Convolutional Networks for Biomedical Image Segmentation [MICCAI 2015] [Code]

Several established geometric deep-learning models are included for design tasks:

Graph-UNet - Graph U-Nets [ICML 2019] [Code]
GraphSAGE - Inductive Representation Learning on Large Graphs [NeurIPS 2017] [Code]
PointNet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [CVPR 2017] [Code]

The library also includes the following graph neural network:

MeshGraphNet LEARNING MESH-BASED SIMULATION WITH GRAPH NETWORKSICLR 2021 [Code]

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

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

hf download OneScience-Group/CFD_Benchmark --local-dir ./CFD_Benchmark 
cd CFD_Benchmark

Set Up the Runtime Environment

DCU Environment

# 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

# 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] and [geo-FNO] are available from this link.

The PDEBench [NeurIPS 2022 Track dataset and benchmark], used for benchmarking autoregressive tasks, is available from this link.

The ShapeNet-Car [TOG 2018] benchmark dataset for industrial design tasks is available from [this link].

The BubbleML [Multiphase Multiphysics Dataset], designed for research on multiphysics phase-transition phenomena, is available from [this link].

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:

hf download --repo-type dataset OneScience-Group/cfd_benchmark  --local-dir ./data

Training

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

python scripts/inference.py

Inference loads the trained weights referenced by paths.weight_path and writes the metrics to:

./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.
This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope.

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