7 citations · 13 across the 8 of their papers we have counts for
3 papers · 1 filter
Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström +2
Embedding non-restrictive prior knowledge, such as energy conservation laws, into learning methods is a key motive to construct physically consistent dynamics models from limited d…
Dual Iterative Learning Control for Multiple-Input Multiple-Output Dynamics with Validation in Robotic Systems
Jan-Hendrik Ewering, Alessandro Papa, Simon F. G. Ehlers +2
Solving motion tasks autonomously and accurately is a core ability for intelligent real-world systems. To achieve genuine autonomy across multiple systems and tasks, key challenges…
Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge
Björn Volkmann, Jan-Hendrik Ewering, Michael Meindl +2
Accuracy and generalization capabilities are key objectives when learning dynamical system models. To obtain such models from limited data, current works exploit prior knowledge an…