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OneScience-Group/MeshGraphNet

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face
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<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 CaseDescription
External flow predictionPredict velocity, pressure, and other flow variables at mesh nodes
Structural deformation simulationPredict the displacement, stress, and deformation of loaded structures
Cloth dynamicsSimulate the motion of deformable objects such as flexible membranes and cloth
ModelScope/OneCode executionDownload 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

bash
hf download OneScience-Group/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
hf download --repo-type dataset OneScience-Group/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

PlatformOneScience RepositorySkills Repository
Giteehttps://gitee.com/onescience-ai/onesciencehttps://gitee.com/onescience-ai/oneskills
GitHubhttps://github.com/onescience-ai/OneSciencehttps://github.com/onescience-ai/oneskills

Citations and License