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Ernst, A., &; Schmidt, K.-U. (2024). Transitivity in finite general linear groups. Mathematische Zeitschrift, 307(45).https://doi.org/10.1007/s00209-024-03511-x 
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Peitz, S., &; Bieker, K. (2023). On the Universal Transformation of Data-Driven Models to Control Systems. Automatica, 149, Article 110840.https://doi.org/10.1016/j.automatica.2022.110840 
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Gebken, B., Bieker, K., &; Peitz, S. (2023). On the structure of regularization paths for piecewise differentiable regularization terms. Journal of Global Optimization, 85(3), 709–741.https://doi.org/10.1007/s10898-022-01223-2 
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Cappello, C., Naserasr, R., Steffen, E., &; Wang, Z. (2023). Critically 3-frustrated signed graphs. In arXiv:2304.10243.
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Silva, H. O. M., Rubert, D. P., Araujo, E., Steffen, E., Doerr, D., &; Martinez, F. V. (2023). Algorithms for the genome median under a restricted measure of rearrangement. RAIRO - Operations Research, 57(3), 1045–1058.https://doi.org/10.1051/ro/2023052 
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Ma, Y., Mattiolo, D., Steffen, E., &; Wolf, I. H. (2023). Sets of r-graphs that color all r-graphs. In arXiv:2305.08619.
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von der Gracht, S., Nijholt, E., &; Rink, B. (n.d.). A parametrisation method for high-order phase reduction in coupled  oscillator networks. In arXiv:2306.03320.
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Ma, Y., Mattiolo, D., Steffen, E., &; Wolf, I. H. (2023). Pairwise Disjoint Perfect Matchings in r-Edge-Connected r-Regular Graphs. SIAM Journal on Discrete Mathematics, 37(3), 1548–1565.https://doi.org/10.1137/22m1500654 
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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 
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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 
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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 
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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 
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Nüske, F., Peitz, S., Philipp, F., Schaller, M., &; Worthmann, K. (2023). Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science, 33, Article 14.https://doi.org/10.1007/s00332-022-09862-1 
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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 
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von der Gracht, S., Nijholt, E., &; Rink, B. (2023). Hypernetworks: Cluster Synchronization Is a Higher-Order Effect. SIAM Journal on Applied Mathematics, 83(6), 2329–2353.https://doi.org/10.1137/23m1561075 
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von der Gracht, S., Nijholt, E., &; Rink, B. (2023). Higher order interactions lead to ctant” synchrony breaking. In arXiv:2311.17186.
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Klüners, J., &; Wang, J. (2023). Idélic Approach in Enumerating Heisenberg Extensions. La Matematica.https://doi.org/10.1007/s44007-023-00067-w 
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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 
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Gerlach, R., von der Gracht, S., &; Dellnitz, M. (n.d.). On the Dynamical Hierarchy in Gathering Protocols with Circulant  Topologies. In arXiv:2305.06632.
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