Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data

Understand

This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data.

• The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time.
• The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data.
• Our learning method is evaluated on the motion of a pendulum -- a well studied baseline for nonlinear model identification and control with continuous states and control inputs -- measured via high-dimensional noisy RGB images.