OneScience/EagleMeshTransformer

EagleMeshTransformer

Model Overview

EagleMeshTransformer is a multiscale Mesh Transformer developed by the LIRIS research laboratory in Lyon, France, for fluid prediction on dynamic unstructured meshes. It is particularly well suited to unsteady turbulent flows and problems involving long-range dependencies in flow fields.

Paper: EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers.

Model Description

EagleMeshTransformer uses a multiscale Mesh Transformer architecture trained on the EAGLE dataset to predict velocity and pressure fields in complex unsteady flows.

Use Cases

Use Case	Description
Unsteady turbulent flow prediction	Predict velocity and pressure fields in complex, aperiodic turbulent flows involving drones, jets, wakes, and similar systems
Unstructured-mesh simulation	Process irregular mesh data defined on complex geometries
CFD surrogate acceleration	Provide fast approximations of conventional Navier–Stokes and CFD simulations
Long-horizon physical prediction	Predict the evolution of physical states through autoregressive rollouts

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

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

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

Synthetic Data Validation

The default configuration points to the synthetic data directory in this repository and sets training.max_epoch to 1. Generate a minimal EAGLE NPZ dataset to validate the training and inference pipelines:

python scripts/fake_data.py

Training Data

The OneScience community provides the EAGLE dataset for training. Download it with the command below and verify that the data path in conf/config.yaml is configured correctly.

modelscope download --dataset OneScience/eagle --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 best_model.pth in the weight/ directory.

Model Weights

This repository will provide pretrained EagleMeshTransformer weights 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 EagleMeshTransformer paper: EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers.
• This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope.