---
frameworks:
- ""
language:
- en
license: apache-2.0
tags:
- OneScience
- fluid dynamics
- external flow prediction
- unstructured-mesh simulation

---
<p align="center">
  <strong>
    <span style="font-size: 30px;">MeshGraphNet</span>
  </strong>
</p>

# Model Overview

MeshGraphNets is a graph neural network developed by DeepMind for mesh-based physical simulation. It rapidly predicts the dynamics of complex physical systems, including fluids, structures, and cloth.

Paper: Learning Mesh-Based Simulation with Graph Networks
https://arxiv.org/abs/2010.03409

# Model Description
MeshGraphNets uses an encoder–processor–decoder graph-network architecture trained on trajectories from fluid, structural, and cloth simulations to perform long-horizon dynamical simulation of complex physical systems.

## Use Cases

| Use Case | Description |
|---|---|
| External flow prediction | Predict velocity, pressure, and other flow variables at mesh nodes |
| Structural deformation simulation | Predict the displacement, stress, and deformation of loaded structures |
| Cloth dynamics | Simulate the motion of deformable objects such as flexible membranes and cloth |
| 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.
- 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/MeshGraphNet --local_dir ./MeshGraphNet 
cd MeshGraphNet 
```

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

```bash
modelscope download --dataset OneScience/cylinder_flow --local_dir ./data
```

### Training

Single GPU:

```bash
python scripts/train.py
```

Multiple GPUs:

```bash
torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train.py
```

Training saves `.pth` files under `weight/checkpoints`.

### Model Weights
This repository will provide weights trained on the `cylinder_flow` dataset in the `weights/` directory. The weights will be uploaded soon.

### Inference

```bash
python scripts/inference.py
```

Inference results are saved to `result/output/`.

### Evaluation and Visualization

```bash
python scripts/result.py
```


# 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 MeshGraphNet paper: [Learning Mesh-Based Simulation with Graph Networks](https://arxiv.org/abs/2010.03409).
- This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope.