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

Dierkes E, Offen C, Ober-Blöbaum S, Flaßkamp K. Hamiltonian Neural Networks with Automatic Symmetry Detection. Chaos. 2023;33(6). doi:10.1063/5.0142969

Sprenger A. Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen. Universität Paderborn; 2023. doi:10.17619/UNIPB/1-1787

Baci A, Heindorf S. Accelerating Concept Learning via Sampling. In: CIKM. ; 2023.

Peitz S, Harder H, Nüske F, Philipp F, Schaller M, Worthmann K. Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems. arXiv:230715325. Published online 2023.

Karakaya Y, Somnitz H, Hermsen A, Gonchikzhapov M, Kasper T. Revisiting the initial reaction rates for TMS combustion and a new evidence for metastable silica nanoparticles in the gas-phase synthesis. Applications in Energy and Combustion Science. 2023;14. doi:10.1016/j.jaecs.2023.100152

Nüske F, Peitz S, Philipp F, Schaller M, Worthmann K. Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science. 2023;33. doi:10.1007/s00332-022-09862-1

Hotegni SS, Peitz S, Berkemeier MB. Multi-Objective Optimization for Sparse Deep Neural Network Training. arXiv:230812243. Published online 2023.

Gonchikzhapov M, Kasper T. Thermal and chemical structure of ethanol and 2-ethylhexanoic acid/ethanol SpraySyn flames. Applications in Energy and Combustion Science. 2023;15. doi:10.1016/j.jaecs.2023.100174

Wallscheid O, Peitz S, Stenner J, et al. ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software. 2023;8(89). doi:10.21105/joss.05616