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GD-VAE
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.. figure:: overview.png
   :align: center

**Geometric Dynamic Variational Autoencoders (GD-VAEs) 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.

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

*GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions,* 
R. Lopez and P. J. Atzberger, (preprint), (2022)  `[arXiv] <http://arxiv.org/abs/2206.05183/>`_ .

.. | @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}
.. | }

.. toctree::
   :maxdepth: 1
   :caption: Package Reference

   geo_map.rst
   vae.rst
   nn.rst
   utils.rst
   log.rst
   indices.rst

.. toctree::
   :maxdepth: 1
   :caption: Links

   GitHub for Code / Examples <https://github.com/gd-vae/gd-vae/>
   Atzberger Research Group <http://atzberger.org>