This file contains the ipynb for the EQL autoencoder project supervised by Sam Kim and Peter Lu in Fall 2020, as well as the framework for the EQL machinery, which has been edited to be Tensorflow 2.0-adaptable.
The project was ran on Google Colabs, and contains three main parts: an initialization for the generation of high-dimensional datasets based from the PDEs governing the Lorenz system, the machinery itself, and a snippet of code which graphs the results obtained by the equation given by the three equations given by the autoencoder for the latent space.
functions, helpers, pretty_print, regularization, and symbolic_network are all util functions.