Geometric Dynamic Variational Autoencoders (GD-VAEs) 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.
machine-learning
deep-learning
point-cloud
neural-networks
dynamical-systems
manifold-learning
dimension-reduction
geometric-algorithms
topological-data-analysis
variational-autoencoder
parameterized-systems
geometric-deep-learning
pytorch-implementation
autoencoder-neural-network
latent-spaces
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Updated
Aug 30, 2023 - TeX