gd-vae-pytorch 1.0.4

Geometric Dynamic Variational Autoencoders (GD-VAEs).

Geometric Dynamic Variational Autoencoders (GD-VAE) package provides machine learning methods for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations. Package is for use in pytorch.

If you find these codes or methods helpful for your project, please cite:

GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Non-linear Dynamics and Dimension Reductions, R. Lopez and P. J. Atzberger, arXiv:2206.05183, (2022), [arXiv].

@article{lopez_atzberger_gd_vae_2022,
  title={GD-VAEs: Geometric Dynamic Variational Autoencoders for 
  Learning Non-linear Dynamics and Dimension Reductions},
  author={Ryan Lopez, Paul J. Atzberger},
  journal={arXiv:2206.05183},  
  month={June},
  year={2022},
  url={http://arxiv.org/abs/2206.05183}
}

For source code, examples, and additional information see https://github.com/gd-vae/gd-vae and http://atzberger.org.