ChaosVAE is a deep learning model based on Variational Autoencoders (VAEs) that can learn and generate chaotic systems. It is designed to learn the dynamics of complex systems from time series data and can be used to generate new samples from the learned dynamics.
git clone https://github.com/a-hamdi/ChaosVAE
cd ChaosVAE
python3 -m venv env source env/bin/activate
you can generate it using:
the subroutine rkdumb() taken from Numerical Recipes, with a step size of 0.01.
from the lorenz equations:
dx/dt = sigma * (y - x)
dy/dt = r * x - y - x * z
dz/dt = x * y - b * z
Or you can find the data here:
https://physics.emory.edu/faculty/weeks/research/tseries1.html
This project is licensed under the MIT License.
I'm still working on the project and the rest of it is local and private.
If you want to help send me a message!