Subgrid Transport Neural Network

This repository contains code for the paper Physical invariance in neural networks for subgrid-scale scalar flux modeling (2020).

<img src="data/fig/subgrid_turbulence.png?raw=true" width="500">

Dataset

The dataset is available here and should be extracted in data/ by default. It contains DNS data filtered at different resolutions, even if this paper only deal with a filter size equal to 8.

Usage

Three notebooks can be found in notebook/ that shows how to load the data, train a model and evaluate pretrained version with the different metrics presented in the paper. The source of the SGTNN model can be found otherwise in src/.

Citing

If you find this code useful in your research, consider citing with

@article{frezat2020physical,
  title={Physical invariance in neural networks for subgrid-scale scalar flux modeling},
  author={Frezat, Hugo and Balarac, Guillaume and Sommer, Julien Le and Fablet, Ronan and Lguensat, Redouane},
  journal={arXiv preprint arXiv:2010.04663},
  year={2020}
}