A convolutional encoder-decoder-based transformer model has been developed to autoregressively train on spatio-temporal data of turbulent flows. It works by predicting future fluid flow fields from the previously predicted fluid flow field to ensure long-term predictions without diverging. The model exhibits significant agreements for \textit{a priori} assessments, and the \textit{a posterior} predictions, after a considerable number of simulation steps, exhibit predicted variances. Autoregressive training and prediction of \textit{a posteriori} states is the primary step towards the development of more complex data-driven turbulence models and simulations.
Article: https://arxiv.org/abs/2209.08052
git clone https://github.com/aakash30jan/Spatio-Temporal-Learning-of-Turbulent-Flows.gitIf git is not installed, you can get the source zip with
wget -O Spatio-Temporal-Learning-of-Turbulent-Flows.zip https://github.com/aakash30jan/https://github.com/aakash30jan/Spatio-Temporal-Learning-of-Turbulent-Flows/archive/refs/heads/main.zip
unzip Spatio-Temporal-Learning-of-Turbulent-Flows.zipMake sure you install TF2.0 with GPU support.
Make sure the training data is stored at case_dir
cd src
python3 ./train.py 1 both 2 case2 1 2The file train.py is self-explanatory: We first load the system and user-defined libraries, set the training parameters, load the pre-processed dataset, load the model architectures, define training and validation steps to suit TF2.0, and then perform the training. Make sure cuda-capabale devices and drivers are visible to Tensorflow, you may need to module load cudaxxx depending on the machine configuration.
Problems? Please raise an issue at https://github.com/aakash30jan/Spatio-Temporal-Learning-of-Turbulent-Flows/issues.
Please use https://arxiv.org/abs/2209.08052 for citing this code or article. You may also download this .bib file or copy the following bibtex entry.
@article{patil2022autoregressive,
title={Autoregressive transformers for data-driven spatio-temporal learning of turbulent flows},
author={Patil, Aakash and Viquerat, Jonathan and Hachem, Elie},
journal={arXiv preprint arXiv:2209.08052},
year={2022}
}
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