Symplectic integration of boundary value problems. - Robert I. McLachlan; Christian Offen (Numer. Algorithms, 2019)

Backward error analysis for variational discretisations of partial differential equations. - Robert I. McLachlan; Christian Offen (CoRR, 2020)

Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation. - Robert I. McLachlan; Christian Offen (Found. Comput. Math., 2020)

Learning ODE Models with Qualitative Structure Using Gaussian Processes. - Steffen Ridderbusch; Christian Offen; Sina Ober-Blöbaum; Paul Goulart (CDC, 2021)

Symplectic integration of learned Hamiltonian systems. - Christian Offen; Sina Ober-Blöbaum (CoRR, 2021)

Variational integration of learned dynamical systems. - Sina Ober-Blöbaum; Christian Offen (CoRR, 2021)

Backward error analysis for conjugate symplectic methods. - Robert I. McLachlan; Christian Offen (CoRR, 2022)

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery. - Yana Lishkova; Paul Scherer; Steffen Ridderbusch; Mateja Jamnik; Pietro Liò; Sina Ober-Blöbaum; Christian Offen (CoRR, 2022)

Learning Discrete Lagrangians for Variational PDEs from Data and Detection of Travelling Waves. - Christian Offen; Sina Ober-Blöbaum (GSI (1), 2023)

Hamiltonian Neural Networks with Automatic Symmetry Detection. - Eva Dierkes; Christian Offen; Sina Ober-Blöbaum; Kathrin Flaßkamp (CoRR, 2023)

Learning discrete Lagrangians for variationalPDEs from data and detection of travelling waves. - Christian Offen; Sina Ober-Blöbaum (CoRR, 2023)

Learning of discrete models of variational PDEs from data. - Christian Offen; Sina Ober-Blöbaum (CoRR, 2023)

Variational learning of Euler-Lagrange dynamics from data. - Sina Ober-Blöbaum; Christian Offen (J. Comput. Appl. Math., 2023)

Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning. - Michael Dellnitz; Eyke Hüllermeier; Marvin Lücke; Sina Ober-Blöbaum; Christian Offen; Sebastian Peitz; Karlson Pfannschmidt (SIAM J. Sci. Comput., 2023)

Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification. - Christian Offen (CoRR, 2024)

Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification. - Christian Offen (CoRR, 2024)

Commutator-free Cayley methods. - Sofya Maslovskaya; Christian Offen; Sina Ober-Blöbaum; Pranav Singh; Boris Wembe (CoRR, 2024)

Adaptive higher order reversible integrators for memory efficient deep learning. - Sofya Maslovskaya; Sina Ober-Blöbaum; Christian Offen; Pranav Singh; Boris Wembe (CoRR, 2024)