• Corpus ID: 246035805

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… 

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