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1304 Publications


2021 | Journal Article | LibreCat-ID: 35751
Frischemeier, D., Biehler, R., Podworny, S., & Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with<scp>CODAP</scp>using survey data. Teaching Statistics, 43(S1), S182–S189. https://doi.org/10.1111/test.12283
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2021 | Journal Article | LibreCat-ID: 24786
Schürmann, M., Liebendörfer, M., Gildehaus, L., Schaper, N., Hochmuth, R., Biehler, R., Lankeit, E., Kuklinski, C., & Ruge, J. (2021). Opportunities and Possibilities of a Network of Mathematical Learning and Support Centres in Germany. Sigma Newsletter, 22, 5–6.
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2021 | Journal Article | LibreCat-ID: 45335
Schönherr, J., Schukajlow, S., & Blomberg, J. (2021). Was ist eine gute Skizze? Strategiewissen beim mathematischen Modellieren im Bereich der Geometrie fördern. mathematik lehren, 224, 22–27.
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2021 | Journal Article | LibreCat-ID: 45333
Schönherr, J., Schukajlow, S., Blomberg, J., & Leopold, C. (2021). Does strategic knowledge matter? Effects of strategic knowledge about drawing on students’ modeling competencies in the domain of geometry. Mathematical Thinking and Learning. https://doi.org/10.1080/10986065.2021.2012741
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2021 | Journal Article | LibreCat-ID: 45342
Schönherr, J., Blomberg, J., Schukajlow, S., & Leopold, C. (2021). Do emotions and prior performance facilitate the use of the learner-generated drawing strategy? Effects of enjoyment, anxiety, and intramathematical performance on the use of the drawing strategy and modelling performance. Contemporary Educational Psychology, 65. https://doi.org/10.1016/j.cedpsych.2021.101967
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2020 | Journal Article | LibreCat-ID: 19938
McLachlan, R. I., & Offen, C. (2020). Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation. Foundations of Computational Mathematics, 20(6), 1363–1400. https://doi.org/10.1007/s10208-020-09454-z
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2020 | Journal Article | LibreCat-ID: 19939 | OA
Kreusser, L. M., McLachlan, R. I., & Offen, C. (2020). Detection of high codimensional bifurcations in variational PDEs. Nonlinearity, 33(5), 2335–2363. https://doi.org/10.1088/1361-6544/ab7293
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2020 | Book Chapter | LibreCat-ID: 17411
Flaßkamp, K., Ober-Blöbaum, S., & Peitz, S. (2020). Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach. In O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, & K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation. Cham: Springer. https://doi.org/10.1007/978-3-030-51264-4_9
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2020 | Journal Article | LibreCat-ID: 21819 | OA
Klus, S., Nüske, F., & Hamzi, B. (2020). Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. Entropy. https://doi.org/10.3390/e22070722
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2020 | Journal Article | LibreCat-ID: 16964
Hochmuth, R., Liebendörfer, M., Biehler, R., & Eichler, A. (2020). Das Kompetenzzentrum Hochschuldidaktik Mathematik (khdm). Neues Handbuch Hochschullehre, 95, 117–138.
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2020 | Journal Article | LibreCat-ID: 10596
Schütze, O., Cuate, O., Martín, A., Peitz, S., & Dellnitz, M. (2020). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization, 52(5), 832–855. https://doi.org/10.1080/0305215x.2019.1617286
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2020 | Journal Article | LibreCat-ID: 16288
Klus, S., Nüske, F., Peitz, S., Niemann, J.-H., Clementi, C., & Schütte, C. (2020). Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena, 406. https://doi.org/10.1016/j.physd.2020.132416
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2020 | Book Chapter | LibreCat-ID: 16289
Peitz, S., & Klus, S. (2020). Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator. In Lecture Notes in Control and Information Sciences (Vol. 484, pp. 257–282). Cham: Springer. https://doi.org/10.1007/978-3-030-35713-9_10
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2020 | Journal Article | LibreCat-ID: 16290 | OA
Bieker, K., Peitz, S., Brunton, S. L., Kutz, J. N., & Dellnitz, M. (2020). Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics, 34, 577–591. https://doi.org/10.1007/s00162-020-00520-4
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2020 | Journal Article | LibreCat-ID: 16309
Peitz, S., Otto, S. E., & Rowley, C. W. (2020). Data-Driven Model Predictive Control using Interpolated Koopman  Generators. SIAM Journal on Applied Dynamical Systems, 19(3), 2162–2193. https://doi.org/10.1137/20M1325678
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2020 | Book Chapter | LibreCat-ID: 13108
Schumacher, J., & Rezat, S. (2020). Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge. In G. Kadunz (Ed.), Zeichen und Sprache im Mathematikunterricht. Springer. https://doi.org/10.1007/978-3-662-61194-4_5
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2020 | Conference Paper | LibreCat-ID: 31873
Schumacher, J. (2020). Deduktion und Abduktion beim diagrammatischen Schließen – das didaktische Potential der Peirceschen Semiotik. https://doi.org/10.17877/DE290R-21555
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2020 | Journal Article | LibreCat-ID: 16710 | OA
Gerlach, R., Ziessler, A., Eckhardt, B., & Dellnitz, M. (2020). A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors. SIAM Journal on Applied Dynamical Systems, 705–723. https://doi.org/10.1137/19m1247139
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2020 | Journal Article | LibreCat-ID: 34789
Amiri, H., Glöckner, H., & Schmeding, A. (2020). Lie groupoids of mappings taking values in a Lie groupoid. Archivum Mathematicum, 56(5), 307–356. https://doi.org/10.5817/AM2020-5-307
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2020 | Journal Article | LibreCat-ID: 34787
Glöckner, H., & Masbough, N. (2020). Products of regular locally compact spaces are k_R-spaces. Topology Proceedings, 55, 35–38.
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