Offen, C., &; Ober-Blöbaum, S. (2024). Learning of discrete models of variational PDEs from data. Chaos, 34(1), Article 013104.https://doi.org/10.1063/5.0172287 

Leyendecker, S., Maslovskaya, S., Ober-Blöbaum, S., Almagro, R. T. S. M. de, &; Szemenyei, F. O. (2024). A new Lagrangian approach to control affine systems with a quadratic Lagrange term. Journal of Computational Dynamics, 0(0), 0–0.https://doi.org/10.3934/jcd.2024017 

Offen, C., &; Ober-Blöbaum, S. (2023). Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves. In F. Nielsen &; F. Barbaresco (Eds.), Geometric Science of Information (Vol. 14071, pp. 569–579). Springer, Cham.https://doi.org/10.1007/978-3-031-38271-0_57 

Ober-Blöbaum, S., &; Offen, C. (2023). Variational Learning of Euler–Lagrange Dynamics from Data. Journal of Computational and Applied Mathematics, 421, 114780.https://doi.org/10.1016/j.cam.2022.114780 

Dierkes, E., Offen, C., Ober-Blöbaum, S., &; Flaßkamp, K. (2023). Hamiltonian Neural Networks with Automatic Symmetry Detection. Chaos, 33(6), Article 063115.https://doi.org/10.1063/5.0142969 

Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., &; Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), 9–5.https://doi.org/10.1137/21M1412682 

Lishkova, Y., Ober-Blöbaum, S., &; Leyendecker, S. (2023). Multirate Discrete Mechanics and Optimal Control for a Flexible Satelite Model . Invited Mathematical Magazine Article.

Lishkova, Y., Bando, M., &; Ober-Blöbaum, S. (2023). Variational approach for modelling and optimal control of electrodynamic tether motion.

Lishkova, Y., Scherer, P., Ridderbusch, S., Jamnik, M., Liò, P., Ober-Blöbaum, S., &; Offen, C. (2023). Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery. IFAC-PapersOnLine, 56(2), 3203–3210.https://doi.org/10.1016/j.ifacol.2023.10.1457 

Cresson, J., Jiménez, F., &; Ober-Blöbaum, S. (2022). Continuous and discrete Noether’s fractional conserved quantities for restricted calculus of variations. AIMS, 14(1), 57–89.

Faulwasser, T., Flaßkamp, K., Ober-Blöbaum, S., Schaller, M., &; Worthmann, K. (2022). Manifold turnpikes, trims, and symmetries. Mathematics of Control, Signals, and Systems, 34, 759–788.

Offen, C., &; Ober-Blöbaum, S. (2022). Symplectic integration of learned Hamiltonian systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(1).https://doi.org/10.1063/5.0065913 

Vertovec, N., Ober-Blöbaum, S., &; Margellos, K. (2022). Verification of safety critical control policies using kernel methods. 1870–1875.

Ober-Blöbaum, S., &; Vermeeren, M. (2021). Superconvergence of galerkin variational integrators. In IFAC-PapersOnLine (Ed.), 7th IIFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control LHMNC: Vol. 54(19) (pp. 327–333).

Ober-Blöbaum, S., &; Peitz, S. (2021). Explicit multiobjective model predictive control for nonlinear systems  with symmetries. International Journal of Robust and Nonlinear Control, 31(2), 380–403.https://doi.org/10.1002/rnc.5281 

Lishkova, Y., Cannon, M., &; Ober-Blöbaum, S. (2021). A multirate variational approach to Nonlinear MPC.

Offen, C., &; Ober-Blöbaum, S. (2021). Bifurcation preserving discretisations of optimal control problems: Vol. 54(19) (pp. 334–339).https://doi.org/10.1016/j.ifacol.2021.11.099 

Ridderbusch, S., Offen, C., Ober-Blöbaum, S., &; Goulart, P. (2021). Learning ODE Models with Qualitative Structure Using Gaussian Processes . 2021 60th IEEE Conference on Decision and Control (CDC), 2896.https://doi.org/10.1109/CDC45484.2021.9683426 

Vertovec, N., Ober-Blöbaum, S., &; Margellos, K. (n.d.). Multi-objective minimum time optimal control for low-thrust trajectory design. 1975–1980.

Jiménez, F., &; Ober-Blöbaum, S. (2021). Fractional Damping Through Restricted Calculus of Variations. Nichtlineare Sci 31, 46.