630 citations · 1.2k across the 74 of their papers we have counts for
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Structure-Preserving Learning Using Gaussian Processes and Variational Integrators
Jan Brüdigam, Martin Schuck, Alexandre Capone +2
Gaussian process regression is increasingly applied for learning unknown dynamical systems. In particular, the implicit quantification of the uncertainty of the learned model makes…
Diffeomorphically Learning Stable Koopman Operators
Petar Bevanda, Max Beier, Sebastian Kerz +3
System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling. Due to the operator's linearity, a ran…
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes
Alejandro J. Ordóñez-Conejo, Armin Lederer, Sandra Hirche
When signals are measured through physical sensors, they are perturbed by noise. To reduce noise, low-pass filters are commonly employed in order to attenuate high frequency compon…
Learning the Koopman Eigendecomposition: A Diffeomorphic Approach
Petar Bevanda, Johannes Kirmayr, Stefan Sosnowski +1
We present a novel data-driven approach for learning linear representations of a class of stable nonlinear systems using Koopman eigenfunctions. By learning the conjugacy map betwe…
Personalized Rehabilitation Robotics based on Online Learning Control
Samuel Tesfazgi, Armin Lederer, Johannes F. Kunz +2
The use of rehabilitation robotics in clinical applications gains increasing importance, due to therapeutic benefits and the ability to alleviate labor-intensive works. However, th…
Linear-Time Contact and Friction Dynamics in Maximal Coordinates using Variational Integrators
Jan Brüdigam, Jana Janeva, Stefan Sosnowski +1
Simulation of contact and friction dynamics is an important basis for control- and learning-based algorithms. However, the numerical difficulties of contact interactions pose a cha…