OneScience/MeshGraphNet

MeshGraphNet

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

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

modelscope download --model OneScience/MeshGraphNet --local_dir ./MeshGraphNet 
cd MeshGraphNet 

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

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:

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

Training

Single GPU:

python scripts/train.py

Multiple GPUs:

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

python scripts/inference.py

Inference results are saved to result/output/.

Evaluation and Visualization

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