Multiphoton, multimode state classification for nonlinear optical circuits 
D. Kopylov, C. Offen, L. Ares, B.E. Wembe Moafo, S. Ober-Blöbaum, T. Meier, P. Sharapova, J. Sperling, (n.d.).

Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification
C. Offen, Mathematics of Computation (2025).

Learning of discrete models of variational PDEs from data
C. Offen, S. Ober-Blöbaum, Chaos 34 (2024).

Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification
C. Offen, (n.d.).

Commutator-free Cayley methods
S. Maslovskaya, C. Offen, S. Ober-Blöbaum, P. Singh, B.E. Wembe Moafo, (n.d.).

Adaptive higher order reversible integrators for memory efficient deep learning
S. Maslovskaya, S. Ober-Blöbaum, C. Offen, P. Singh, B.E. Wembe Moafo, (2024).

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
Y. Lishkova, P. Scherer, S. Ridderbusch, M. Jamnik, P. Liò, S. Ober-Blöbaum, C. Offen, in: IFAC-PapersOnLine, Elsevier, 2023, pp. 3203–3210.

Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves
C. Offen, S. Ober-Blöbaum, in: F. Nielsen, F. Barbaresco (Eds.), Geometric Science of Information, Springer, Cham., 2023, pp. 569–579.

Variational Learning of Euler–Lagrange Dynamics from Data
S. Ober-Blöbaum, C. Offen, Journal of Computational and Applied Mathematics 421 (2023) 114780.

Backward error analysis for conjugate symplectic methods
R. McLachlan, C. Offen, Journal of Geometric Mechanics 15 (2023) 98–115.

Hamiltonian Neural Networks with Automatic Symmetry Detection
E. Dierkes, C. Offen, S. Ober-Blöbaum, K. Flaßkamp, Chaos 33 (2023).

Efficient time stepping for numerical integration using reinforcement  learning
M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) 9–5.

Backward error analysis for variational discretisations of partial  differential equations
R.I. McLachlan, C. Offen, Journal of Geometric Mechanics 14 (2022) 447–471.

Symplectic integration of learned Hamiltonian systems
C. Offen, S. Ober-Blöbaum, Chaos: An Interdisciplinary Journal of Nonlinear Science 32(1) (2022).

Bifurcation preserving discretisations of optimal control problems
C. Offen, S. Ober-Blöbaum, 54(19) (2021) 334–339.

Learning ODE Models with Qualitative Structure Using Gaussian Processes 
S. Ridderbusch, C. Offen, S. Ober-Blöbaum, P. Goulart, in: 2021 60th IEEE Conference on Decision and Control (CDC), IEEE, 2021, p. 2896.

Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation
R.I. McLachlan, C. Offen, Foundations of Computational Mathematics 20 (2020) 1363–1400.

Detection of high codimensional bifurcations in variational PDEs
L.M. Kreusser, R.I. McLachlan, C. Offen, Nonlinearity 33 (2020) 2335–2363.

Analysis of Hamiltonian boundary value problems and symplectic integration
C. Offen, Analysis of Hamiltonian Boundary Value Problems and Symplectic Integration, Massey University, Palmerston North, New Zealand, 2020.

Symplectic integration of PDEs using Clebsch variables
R.I. McLachlan, C. Offen, B.K. Tapley, Journal of Computational Dynamics 6 (2019) 111–130.