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https://github.com/dynamicslab/normalformae


https://github.com/dynamicslab/normalformae

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# Normal form Autoencoder
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](https://arxiv.org/abs/2106.05102)

# Reproduce results
Download datasets from [here](https://doi.org/10.4121/14790657.v1), 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.

## How to use
- If you have Linux/Mac, run the following on your terminal to install `Julia` in one command
```terminal
bash -ci "$(curl -fsSL https://raw.githubusercontent.com/abelsiqueira/jill/master/jill.sh)"
```
from [here](https://github.com/abelsiqueira/jill).
- Clone this package and enter the directory. Run `julia` on your terminal.
- Now run the following:
```julia
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](https://docs.julialang.org/en/v1/stdlib/REPL/#man-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](https://docs.julialang.org/en/v1/manual/getting-started/).
```terminal
julia -i run/run_nf.jl
```