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OneScience-Group/
CFDBench

Name: CFDBench
Rating: 2.5 (24 reviews)

OneScience

fluid dynamics

large-scale fluid dynamics benchmark

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CFDBench

Model Overview

CFDBench is a large-scale benchmark developed by researchers at Tsinghua University to evaluate machine-learning methods for computational fluid dynamics. It focuses on model generalization across different boundary conditions, fluid properties, and geometric configurations.

Paper: CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics

Model Description

CFDBench is built from several representative computational fluid dynamics datasets spanning diverse boundary conditions, fluid properties, and geometries. It evaluates both flow-field prediction performance and the generalization capabilities of machine-learning methods.

Use Cases

Use Case	Description
CFD surrogate benchmarking	Compare the flow-field prediction capabilities of neural operators and other deep-learning models using consistent data splits and metrics
Autoregressive flow evolution	Predict subsequent two-dimensional velocity fields step by step from the current grid-based field and assess error accumulation over multiple steps
Non-autoregressive field queries	Predict velocity directly at target locations from operating parameters and spatiotemporal coordinates, enabling evaluation over long time horizons

Supported Models

Type	`root.model.name`	Training Entry Point
Non-autoregressive	`ffn`	`python scripts/train.py`
Non-autoregressive	`deeponet`	`python scripts/train.py`
Autoregressive	`auto_ffn`	`python scripts/train_auto.py`
Autoregressive	`auto_deeponet`	`python scripts/train_auto.py`
Autoregressive	`auto_edeeponet`	`python scripts/train_auto.py`
Autoregressive	`auto_deeponet_cnn`	`python scripts/train_auto.py`
Autoregressive	`resnet`	`python scripts/train_auto.py`
Autoregressive	`unet`	`python scripts/train_auto.py`
Autoregressive	`fno`	`python scripts/train_auto.py`

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/CFDBench --local-dir ./CFDBench
cd CFDBench

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 cfdbench dataset for training. Download it with the command below and verify that the data path in config/config.yaml is configured correctly:

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

Training

Autoregressive Training

The default configuration sets root.model.name: fno, so use the autoregressive training entry point:

python scripts/train_auto.py

To select another autoregressive model, edit conf/config.yaml:

yaml

root:
  model:
    name: "auto_ffn"

Non-autoregressive Training

python scripts/train.py --model deeponet

Model Weights

This repository will provide pretrained CFDBench model weights in the weights/ directory. The weights will be uploaded soon.

Inference and Visualization

The inference script automatically selects the task type from the current model name and reads the following path by default:

./weight/<model.name>.pt

Default FNO inference and visualization:

bash

python scripts/inference.py
python scripts/result.py

Non-autoregressive models can likewise be selected through a command-line argument:

bash

python scripts/inference.py --model ffn
python scripts/result.py --model ffn

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

Original CFDBench paper: CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics
Original CFDBench code repository: https://github.com/luo-yining/CFDBench
This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope. Before public redistribution, verify the applicable licensing requirements of the upstream project.