# Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES

@inproceedings{Guan2022LearningPS, title={Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES}, author={Yifei Guan and Adam Subel and Ashesh Chattopadhyay and Pedram Hassanzadeh}, year={2022} }

We demonstrate how incorporating physics constraints into convolutional neural networks (CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy simulations (LES) in the small-data regime (i.e., when the availability of high-quality training data is limited). Using several setups of forced 2D turbulence as the testbeds, we examine the a priori and a posteriori performance of three methods for incorporating physics: 1) data augmentation (DA), 2) CNN with group…

## One Citation

Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow

- Computer ScienceArXiv
- 2022

Novel analyses and a new framework are presented that explain the physics learned in TL and provide a framework to guide TL for wide-ranging applications in science and engineering, such as climate change modeling.

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