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 

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 

McLachlan, R. I., &; Offen, C. (2022). Backward error analysis for variational discretisations of partial  differential equations. Journal of Geometric Mechanics, 14(3), 447–471.https://doi.org/10.3934/jgm.2022014 

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 

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 

McLachlan, R. I., &; Offen, C. (2020). Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation. Foundations of Computational Mathematics, 20(6), 1363–1400.https://doi.org/10.1007/s10208-020-09454-z 

Kreusser, L. M., McLachlan, R. I., &; Offen, C. (2020). Detection of high codimensional bifurcations in variational PDEs. Nonlinearity, 33(5), 2335–2363.https://doi.org/10.1088/1361-6544/ab7293 

Offen, C. (2020). Analysis of Hamiltonian boundary value problems and symplectic integration. Palmerston North, New Zealand: Massey University.

McLachlan, R. I., Offen, C., &; Tapley, B. K. (2019). Symplectic integration of PDEs using Clebsch variables. Journal of Computational Dynamics, 6(1), 111–130.https://doi.org/10.3934/jcd.2019005 

McLachlan, R. I., &; Offen, C. (2018). Bifurcation of solutions to Hamiltonian boundary value problems. Nonlinearity, 2895–2927.https://doi.org/10.1088/1361-6544/aab630 

McLachlan, R. I., &; Offen, C. (2018). Symplectic integration of boundary value problems. Numerical Algorithms, 1219–1233.https://doi.org/10.1007/s11075-018-0599-7 

Offen, C. (n.d.). Local intersections of Lagrangian manifolds correspond to catastrophe  theory. In arXiv:1811.10165.

McLachlan, R. I., &; Offen, C. (2018). Hamiltonian boundary value problems, conformal symplectic symmetries, and conjugate loci. New Zealand Journal of Mathematics, 48, 83–99.https://doi.org/10.53733/34