Wu, X., Kenter, T., Schade, R., Kühne, T., &; Plessl, C. (2023). Computing and Compressing Electron Repulsion Integrals on FPGAs. 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 162–173.https://doi.org/10.1109/FCCM57271.2023.00026 
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DOI: 10.1109/FCCM57271.2023.00026
arXiv: 2303.13632

Schade, R., Kenter, T., Elgabarty, H., Lass, M., Kühne, T., &; Plessl, C. (2023). Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics. The International Journal of High Performance Computing Applications, Article 109434202311776.https://doi.org/10.1177/10943420231177631 
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Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.https://doi.org/10.17619/UNIPB/1-1780  
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Kress, C., Schwabe, T., Silberhorn, C., &; Scheytt, J. C. (2023). Generation of 100 GHz Periodic Nyquist Pulses using Cascaded Mach-Zehnder Modulators in a Silicon Electronic-Photonic Platform. https://doi.org/10.1364/CLEO_SI.2023.SF1P.6__Conference on Lasers and Electro-Optics (CLEO) 2023.https://doi.org/10.1364/CLEO_SI.2023.SF1P.6__Conference on Lasers and Electro-Optics (CLEO), San Jose, CA, USA.https://doi.org/10.1364/CLEO_SI.2023.SF1P.6 
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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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arXiv: 2302.08232 

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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arXiv: 2112.12619

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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DOI: 10.3934/jgm.2023005
arXiv: 2201.03911

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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arXiv: 2301.07928

Sprenger, A. (2023). Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen. Universität Paderborn.https://doi.org/10.17619/UNIPB/1-1787 
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Sherif, M., Morim da Silva, A. A., Pestryakova, S., Ahmed, A. F. A., Niemann, S., &; Ngonga Ngomo, A.-C. (2023). IngridKG: A FAIR Knowledge Graph of Graffiti. LibreCat University.https://doi.org/10.5281/ZENODO.7560242 
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Peitz, S., Harder, H., Nüske, F., Philipp, F., Schaller, M., &; Worthmann, K. (2023). Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems. In arXiv:2307.15325.
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Greiff, C., Mateos-Núñez, D., Simoni, R., González-Huici, M., Kruse, S., Scheytt, J. C., Kolk, K., Höller, C., Kurz, H. G., Meinecke, M.-M., &; Gisder, T. (2023). Calibration of Large Coherent MIMO Radar Arrays: Channel Imbalances and 3D Antenna Positions. 2023 24th International Radar Symposium (IRS). 2023 24th International Radar Symposium (IRS), Berlin, Germany.https://doi.org/10.23919/IRS57608.2023.10172475 
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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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Hotegni, S. S., Peitz, S., &; Berkemeier, M. B. (2023). Multi-Objective Optimization for Sparse Deep Neural Network Training. In arXiv:2308.12243.
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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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arXiv: arXiv:2104.03562

Sadeghi-Kohan, S., Hellebrand, S., &; Wunderlich, H.-J. (2023). Low Power Streaming of Sensor Data Using Gray Code-Based Approximate Communication. 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W).https://doi.org/10.1109/dsn-w58399.2023.00056 
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Schwerin, I., &; Häsel-Weide, U. (2023). Second grader´s understanding of doubling and halfing in various representations. In J. Novotna &; H. Moraova (Eds.), International Symposium in Elementary Mathematics Teaching. Proceedings: New Directions in Elementary Mathematics Education (pp. 297–305). Charles University.
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Schmidt, R., Tenberge, C., &; Häsel-Weide, U. (2023). Lehre in Zeiten von Digitalisierung und Inklusion - Beispiele aus drei Fächern. In N. Vöing, R. Schmidt, &; I. Neiske (Eds.), Aktive Teilhabe fördern – ICM und Student Engagement in der Hochschullehre (pp. 297–318). Visual Ink Publishing.
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Schloots, F. M. (2023). Mit dem Leben Schritt halten - Eine Analyse des Wearable-Dispositivs. Springer Fachmedien Wiesbaden.https://doi.org/10.1007/978-3-658-40902-9 
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Wallscheid, O., Peitz, S., Stenner, J., Weber, D., Boshoff, S., Meyer, M., Chidananda, V., &; Schweins, O. (2023). ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software, 8(89), Article 5616.https://doi.org/10.21105/joss.05616 
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