$\beta$-Variational autoencoders and transformers for reduced-order modelling of fluid flow

Introduction

The code in this repository features a Python implementation of reduced-order model (ROM) of turbulent flow using $\beta$-variational autoencoders and transformer neural network. More details about the implementation and results from the training are available in "$\beta$-Variational autoencoders and transformers for reduced-order modelling of fluid flow",Alberto Solera-Rico, Carlos Sanmiguel Vila, M. A. Gómez, Yuning Wang, Abdulrahman Almashjary, Scott T. M. Dawson, Ricardo Vinuesa

Data availabilty

1. We share the down-sampled data in zenodo.

2. We share the pre-trained models of $\beta$-VAE, transformers and LSTM with this repository.

Training and inference

• To train and inference the easy-attention-based transformer, please run:

    python main.py -re 40 -m run -nn easy 
  

Structure

• data: Dataset used for the present study

• lib: The main code used in the present study

• utils: Support functions for visualisation, etc.

• configs: Configurations of hyper parameters for models

• nns: The architecture of neural networks

• res: Storage of prediction results

• figs: Storaging figures output