TL_for_SGS_Models

Introduction

This repository includes the codes to produce datasets and implement the training, LES, and analyses in the accompanying paper Explaining the physics of transfer learning in data-driven turbulence modeling https://doi.org/10.1093/pnasnexus/pgad015. The following links to the datasets that can be used for the training of networks, https://zenodo.org/record/6621142.

Requirements

• python 3.8
  • scipy
  • numpy
• TensorFlow 2
• Keras 2.4.3
• Matlab

Codes

Train BNN (Train_BNN.py)

Code takes in training and validation data sets to train a new BNN from a random initialization. This outputs the trained model as well as the predictions of the trained model on a test set of data.

Transfer Learning (DDP_CNN_TL_Single_Layers.py and DDP_CNN_TL_Two_Layers.py)

Code takes in training and validation data as well as a trained BNN to perform transfer learning. The code for two layers is easily modified to select any combination of any number of layers.

Network Post Processing (Extract_Activations.py, Extract_Activations_Linear.py, and Extract_Weights.py)

These codes all take a trained BNN or TLNN and extract out the weights or activation to a .mat format for later analysis. The code Extract_Activations_Linear.py computes the activations, but removes any nonlinearity after the final layer before outputting activations.

Coupled LES (LES_CNN.py)

This code is used for the online testing. This code takes in a trained NN and an initial condition to generate data from large eddy simulation.

Visualizing Kernel Spectra (Plot_Kernel_Spectra.m)

This take the extracted network weights, computes the kernels with the largest changes due to re-training and plots the spectrum of the kernel from both the BNN and TLNN.

Citation

@article{subel2022explaining, title={Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow}, author={Subel, Adam and Guan, Yifei and Chattopadhyay, Ashesh and Hassanzadeh, Pedram}, journal={arXiv preprint arXiv:2206.03198}, year={2022} }