C. Offen and S. Ober-Blöbaum, “Learning of discrete models of variational PDEs from data,”10.1063/5.0172287__Chaos, vol. 34, no. 1, Art. no. 013104, 2024, doi:10.1063/5.0172287 .

C. Offen, “Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification.” .

C. Offen and S. Ober-Blöbaum, “Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves,”10.1007/978-3-031-38271-0_57__in Geometric Science of Information, Saint-Malo, Palais du Grand Large, France, 2023, vol. 14071, pp. 569–579, doi:10.1007/978-3-031-38271-0_57 .

S. Ober-Blöbaum and C. Offen, “Variational Learning of Euler–Lagrange Dynamics from Data,”10.1016/j.cam.2022.114780__Journal of Computational and Applied Mathematics, vol. 421, p. 114780, 2023, doi:10.1016/j.cam.2022.114780 .

R. McLachlan and C. Offen, “Backward error analysis for conjugate symplectic methods,”10.3934/jgm.2023005__Journal of Geometric Mechanics, vol. 15, no. 1, pp. 98–115, 2023, doi:10.3934/jgm.2023005 .

E. Dierkes, C. Offen, S. Ober-Blöbaum, and K. Flaßkamp, “Hamiltonian Neural Networks with Automatic Symmetry Detection,”10.1063/5.0142969__Chaos, vol. 33, no. 6, Art. no. 063115, 2023, doi:10.1063/5.0142969 .

M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,”10.1137/21M1412682__SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. 9–5, 2023, doi:10.1137/21M1412682 .

Y. Lishkova et al., “Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery,”10.1016/j.ifacol.2023.10.1457__in IFAC-PapersOnLine,10.1016/j.ifacol.2023.10.1457__Yokohama, Japan, 2023, vol. 56, no. 2, pp. 3203–3210, doi:10.1016/j.ifacol.2023.10.1457 .

R. I. McLachlan and C. Offen, “Backward error analysis for variational discretisations of partial  differential equations,”10.3934/jgm.2022014__Journal of Geometric Mechanics, vol. 14, no. 3, pp. 447–471, 2022, doi:10.3934/jgm.2022014 .

C. Offen and S. Ober-Blöbaum, “Symplectic integration of learned Hamiltonian systems,”10.1063/5.0065913__Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 32(1), 2022, doi:10.1063/5.0065913 .

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

S. Ridderbusch, C. Offen, S. Ober-Blöbaum, and P. Goulart, “Learning ODE Models with Qualitative Structure Using Gaussian Processes ,”10.1109/CDC45484.2021.9683426__in 2021 60th IEEE Conference on Decision and Control (CDC), Austin, TX, USA, 2021, p. 2896, doi:10.1109/CDC45484.2021.9683426 .

R. I. McLachlan and C. Offen, “Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation,” Foundations of Computational Mathematics, vol. 20, no. 6, pp. 1363–1400, 2020.

L. M. Kreusser, R. I. McLachlan, and C. Offen, “Detection of high codimensional bifurcations in variational PDEs,” Nonlinearity, vol. 33, no. 5, pp. 2335–2363, 2020.

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

R. I. McLachlan, C. Offen, and B. K. Tapley, “Symplectic integration of PDEs using Clebsch variables,” Journal of Computational Dynamics, vol. 6, no. 1, pp. 111–130, 2019.

R. I. McLachlan and C. Offen, “Bifurcation of solutions to Hamiltonian boundary value problems,” Nonlinearity, pp. 2895–2927, 2018.

R. I. McLachlan and C. Offen, “Symplectic integration of boundary value problems,” Numerical Algorithms, pp. 1219–1233, 2018.

C. Offen, “Local intersections of Lagrangian manifolds correspond to catastrophe  theory,” arXiv:1811.10165. .

R. I. McLachlan and C. Offen, “Hamiltonian boundary value problems, conformal symplectic symmetries, and conjugate loci,”10.53733/34 __New Zealand Journal of Mathematics, vol. 48, pp. 83–99, 2018, doi:10.53733/34  .