Götte, R.-S., &; Timmermann, J. (2023). Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 56(1), 85–90.https://doi.org/10.1016/j.ifacol.2023.02.015 

Götte, R.-S., Klusmann, J. N., &; Timmermann, J. (2023). Data-driven identification of disturbances using a sliding mode observer. Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023, 113–123.https://doi.org/10.5445/KSP/1000162754 

Götte, R.-S., &; Timmermann, J. (2023). Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. IFAC-PapersOnLine, 56(2), 869–874.

Götte, R.-S., &; Timmermann, J. (2022). Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering. 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 67–76.https://doi.org/10.1109/AIRC56195.2022.9836982 

Schön, O., Götte, R.-S., &; Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12), 19–24.https://doi.org/10.1016/j.ifacol.2022.07.282