GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions

10 June 2022

Papers citing "GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions"

6 / 6 papers shown

Title
Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators Blaine Quackenbush P. Atzberger AI4CE 33 0 0 16 Apr 2024
Pulling back symmetric Riemannian geometry for data analysis W. Diepeveen 24 2 0 11 Mar 2024
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics P. Stinis C. Daskalakis P. Atzberger SyDa GAN 27 5 0 07 Feb 2023
Disentangled Generative Models for Robust Prediction of System Dynamics Stathi Fotiadis Mario Lino Valencia Shunlong Hu Stef Garasto C. Cantwell Anil Anthony Bharath DRL OOD CML 16 10 0 26 Aug 2021
MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS P. Atzberger AI4CE 18 0 0 29 Jul 2021
Variational Autoencoders with Riemannian Brownian Motion Priors Dimitris Kalatzis David Eklund Georgios Arvanitidis Søren Hauberg BDL DRL 60 48 0 12 Feb 2020