Jan-Hendrik Ewering

According to our database, Jan-Hendrik Ewering authored at least 9 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations.

Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström, Thomas B. Schön, Thomas Seel

CoRR, May, 2026

Lagrangian Neural Network-Based Control: Improving Robotic Trajectory Tracking via Linearized Feedback.

Manuel Weiss, Alexander Pawluchin, Jan-Hendrik Ewering, Thomas Seel, Ivo Boblan

IEEE Robotics Autom. Lett., March, 2026

Simultaneous State Estimation and Online Model Learning in a Soft Robotic System.

Jan-Hendrik Ewering, Max Bartholdt, Simon F. G. Ehlers, Niklas Wahlström, Thomas B. Schön, Thomas Seel

CoRR, February, 2026

2025

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes.

Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström, Thomas B. Schön, Thomas Seel

CoRR, November, 2025

Dual Iterative Learning Control for Multiple-Input Multiple-Output Dynamics with Validation in Robotic Systems.

Jan-Hendrik Ewering, Alessandro Papa, Simon F. G. Ehlers, Thomas Seel, Michael Meindl

CoRR, September, 2025

Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge.

Björn Volkmann, Jan-Hendrik Ewering, Michael Meindl, Simon F. G. Ehlers, Thomas Seel

CoRR, August, 2025

2024

Integrated Model Predictive Control of High-Speed Railway Running Gears With Driven Independently Rotating Wheels.

Jan-Hendrik Ewering, Christoph Schwarz, Simon F. G. Ehlers, Hans-Georg Jacob, Thomas Seel, Andreas Heckmann

IEEE Trans. Veh. Technol., June, 2024

Reliable State Estimation in a Truck-Semitrailer Combination Using an Artificial Neural Network-Aided Extended Kalman Filter.

Jan-Hendrik Ewering, Zygimantas Ziaukas, Simon F. G. Ehlers, Thomas Seel

Proceedings of the European Control Conference, 2024

Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline.

Jan-Hendrik Ewering, Björn Volkmann, Simon F. G. Ehlers, Thomas Seel, Michael Meindl

Proceedings of the 63rd IEEE Conference on Decision and Control, 2024