GD-VAE

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] .

Package Reference

• geo_map.py
• vae.py
• nn.py
• utils.py
• log.py
• Indices and tables

Links

• GitHub for Code / Examples
• Atzberger Research Group