meshgraphnet_pytorch

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meshgraphnet_pytorch

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# MGN

## Description
This project provides a clean pytorch implementation of [Learning mesh-based simulation with Graph Networks](https://sites.google.com/view/meshgraphnets).

## New Features compared to original MeshGraphNets
- Using [pytorch-geometric](https://pytorch-geometric.readthedocs.io/en/latest/) data structure for graph representation and processing.
- Using [hydra](https://hydra.cc/docs/intro/) for hierarchical configuration and using [Weigts&Biases](https://docs.wandb.ai/) to track and visualize experiments.

## Visuals

 <img src="figures/gt_flag_simple.gif">
 <img src="figures/predict_flag_simple.gif">


 <img src="figures/gt_cylinder_flow.gif">
 <img src="figures/predict_cylinder_flow.gif">

GT: left 
Prediction: right

## Quantitative Results
Table below shows quantitative results of different rollout steps for both dataset cylinder_flow and flag_simple. Our results differ
from the ones published in [Paper](https://sites.google.com/view/meshgraphnets). Results can vary under different hyperparameter settings,
e.g. random seed and learning rate.

| rollout in scale(1e-3) | 1 | 10 | 50 | 100 | 200 | all |
|------------------------------|------| --- | --- |--- |--- |--- |
| cylinder_flow | 3.99 | 8.90 | 18.10 | 25.36 | 34.97 | 63.56 |
| flag_simple | 0.98 | 12.15 | 133.51| 157.07| 157.42| 165.09|

## Installation
More simply you can install this package via packaging tool pip.

```
# download this package
git clone ...
# change directory
cd mgn
# install mgn package
pip install -e .
```

## Usage
You can train models with

```
python example/train.py model=cloth datamodule=flag_simple
```
or
```
python example/train.py model=cfd datamodule=cylinder_flow
```
For evaluating checkpoints see [eval.ipynb](./example/eval.ipynb).

meshgraphnet_pytorch

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