In this repository, you will find the different python scripts to train the available models on the 2D incompressible steady-state RANS solutions over NACA airfoils proposed at the Geometrical and Topological Representation Learning Workshop at ICLR 2022. You can find the paper here.
A better designed and high fidelity version is available here. This updated version should be used for subsequent work.
- Python 3.9.12
- PyTorch 1.11.0 with CUDA 11.3
- PyTorch Geometric 2.0.4
- PyVista 0.34.1
- Seaborn 0.11.2
To train a model, run main.py with the desired model architecture:
python main.py GraphSAGE -s val -n 10
Note that you must have the dataset in folder datasets/ at the root of this repository, you can find the dataset here. You can change the parameters of the models and the training in the params.yaml file. You will find the different plots and scores at the root of the metrics folder.
positional arguments:
model The model you want to train, chose between GraphSAGE, GAT, PointNet, GNO, PointNet++, GUNet, MGNO.
optional arguments:
-h, --help show this help message and exit
-n NMODEL, --nmodel NMODEL
Number of trained models for standard deviation estimation (default: 1)
-w WEIGHT, --weight WEIGHT
Weight in front of the surface loss (default: 1)
-s SET, --set SET Set on which you want the scores and the global coefficients plot, choose between val and test (default: val)
The different results for each are given in the metrics folder and are displayed under the form of a table in the original paper.
Please cite this paper if you use this dataset in your own work.
@inproceedings{
bonnet2022an,
title={An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations},
author={Florent Bonnet and Jocelyn Ahmed Mazari and Thibaut Munzer and Pierre Yser and Patrick Gallinari},
booktitle={ICLR 2022 Workshop on Geometrical and Topological Representation Learning},
year={2022},
url={https://openreview.net/forum?id=rqUUi4-kpeq}
}