Burgers_DDP_and_TL

This repository includes the codes to produce datasets and implement the DDP, DSMAG, and TL referenced in the

accompanying paper Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning (https://doi.org/10.1063/5.0040286). The following links to a dataset that can be used with the given DSMAG and DDP codes, https://zenodo.org/record/4316338.

Stochastic_Burgers_DNS.m

This code creates a DNS dataset. Parameters like the Reynolds number and resolution can be altered easily to create datasets to experiment with transfer learning.

make_training_sets.m and make_forcing.m

This codes generate filtered and coarse grained variables for the training and a posteriori testing of DDP.

calc_bar.m

This code contains a function to take in the DNS dataset and then calculate the filtered variables and subgrid Pi terms.

filter_bar.m

This code contains a function to apply the box filter.

DSMAG.py

This code is an implementation of the Dynamic Smagorinsky LES.

ddp_train_and_test.py

This code trains and runs a posteriori prediction for DDP.

Transfer_Learning.py

This code takes in a set of weights for the ANN used in DDP and retrains it for a different training regime.

Citation

Read more on [arXiv]

Read more on [PoF]

@article{subel2020data,
  title={Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning},
  author={Subel, Adam and Chattopadhyay, Ashesh and Guan, Yifei and Hassanzadeh, Pedram},
  journal={arXiv e-prints},
  pages={arXiv--2012},
  year={2020}
}

@article{subel2021data,
  title={Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning},
  author={Subel, Adam and Chattopadhyay, Ashesh and Guan, Yifei and Hassanzadeh, Pedram},
  journal={Physics of Fluids},
  volume={33},
  number={3},
  pages={031702},
  year={2021},
  publisher={AIP Publishing LLC}
}