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. (n.d.). Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification.

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 

McLachlan, R., &; Offen, C. (2023). Backward error analysis for conjugate symplectic methods. Journal of Geometric Mechanics, 15(1), 98–115.https://doi.org/10.3934/jgm.2023005 

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., 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.