Constructing low-dimensional parameterized representations of high dimensional dynamics using normal forms as a building block by using an autoencoder framework. Neural network training is implemented with Flux.jl, a Julia library. Paper available on arXiv

Download datasets from here, and extract contents to NormalFormAE/NFAEdata. Use the scripts in run to reproduce the results from the paper.

Note you need CUDA to run this package.

• If you have Linux/Mac, run the following on your terminal to install Julia in one command

bash -ci "$(curl -fsSL https://raw.githubusercontent.com/abelsiqueira/jill/master/jill.sh)"

from here.

• Clone this package and enter the directory. Run julia on your terminal.
• Now run the following:

 julia> ] activate .
 julia> ] instantiate

which will automatically install the necessary Julia packages you need.

• Run an example (tests coming soon) via the terminal or REPL Shell mode. Note to run in the REPL Shell mode, you need to use the backspace/delete key to exit out of Pkg mode, and then type a ;. Find out more about running Julia files in the Julia docs.

julia -i run/run_nf.jl