Hüsing, Sven, et al. “Faszination 3D-Film: Entwicklung einer 3D-Konstruktion.” mathematik lehren, vol. 2021, no. 228, Friedrich Verlag, 2021, pp. 23–27.

Schürmann, Mirko, et al. “Consultation Phases in Mathematics Learning and Support Centres.”10.1007/s40753-021-00154-9__International Journal of Research in Undergraduate Mathematics Education, vol. 8, no. 1, Springer Science and Business Media LLC, 2021, pp. 94–120, doi:10.1007/s40753-021-00154-9.

Podworny, Susanne, and Rolf Biehler. “The Process of Actively Building a Model for a Randomization Test – Insights into Learners’ Modeling Activities Based on a Case Study.”10.1080/10986065.2021.1922837__Mathematical Thinking and Learning, vol. 24, no. 4, Informa UK Limited, 2021, pp. 291–311, doi:10.1080/10986065.2021.1922837.

Frischemeier, Daniel, et al. “A First Introduction to Data Science Education in Secondary Schools: Teaching and Learning about Data Exploration With&;scp&;CODAP&;/Scp&;using Survey Data.”10.1111/test.12283__Teaching Statistics, vol. 43, no. , Wiley, 2021, pp. 82–89, doi:10.1111/test.12283.

Schürmann, Mirko, et al. “Opportunities and Possibilities of a Network of Mathematical Learning and Support Centres in Germany.” Sigma Newsletter, vol. 22, 2021, pp. 5–6.

Schönherr, Johanna, et al. “Was ist eine gute Skizze? Strategiewissen beim mathematischen Modellieren im Bereich der Geometrie fördern.” mathematik lehren, vol. 224, 2021, pp. 22–27.

Schönherr, Johanna, et al. “Does Strategic Knowledge Matter? Effects of Strategic Knowledge about Drawing on Students’ Modeling Competencies in the Domain of Geometry.”10.1080/10986065.2021.2012741__Mathematical Thinking and Learning, 2021, doi:10.1080/10986065.2021.2012741.

Schönherr, Johanna, et al. “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.”10.1016/j.cedpsych.2021.101967__Contemporary Educational Psychology, vol. 65, 2021, doi:10.1016/j.cedpsych.2021.101967.

McLachlan, Robert I., and Christian Offen. “Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation.”10.1007/s10208-020-09454-z__Foundations of Computational Mathematics, vol. 20, no. 6, 2020, pp. 1363–400, doi:10.1007/s10208-020-09454-z.

Kreusser, Lisa Maria, et al. “Detection of High Codimensional Bifurcations in Variational PDEs.”10.1088/1361-6544/ab7293__Nonlinearity, vol. 33, no. 5, 2020, pp. 2335–63, doi:10.1088/1361-6544/ab7293.

Flaßkamp, Kathrin, et al. “Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach.”10.1007/978-3-030-51264-4_9__Advances in Dynamics, Optimization and Computation, edited by Oliver Junge et al., Springer, 2020, doi:10.1007/978-3-030-51264-4_9.

Klus, Stefan, et al. “Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator.”10.3390/e22070722__Entropy, 722, 2020, doi:10.3390/e22070722.

Hochmuth, Reinhard, et al. “Das Kompetenzzentrum Hochschuldidaktik Mathematik (Khdm).” Neues Handbuch Hochschullehre, vol. 95, 2020, pp. 117–38.

Schütze, Oliver, et al. “Pareto Explorer: A Global/Local Exploration Tool for Many-Objective Optimization Problems.”10.1080/0305215x.2019.1617286__Engineering Optimization, vol. 52, no. 5, 2020, pp. 832–55, doi:10.1080/0305215x.2019.1617286.

Klus, Stefan, et al. “Data-Driven Approximation of the Koopman Generator: Model Reduction, System Identification, and Control.”10.1016/j.physd.2020.132416__Physica D: Nonlinear Phenomena, vol. 406, 132416, 2020, doi:10.1016/j.physd.2020.132416.

Peitz, Sebastian, and Stefan Klus. “Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator.”10.1007/978-3-030-35713-9_10__Lecture Notes in Control and Information Sciences, vol. 484, Springer, 2020, pp. 257–82, doi:10.1007/978-3-030-35713-9_10.

Bieker, Katharina, et al. “Deep Model Predictive Flow Control with Limited Sensor Data and Online Learning.”10.1007/s00162-020-00520-4__Theoretical and Computational Fluid Dynamics, vol. 34, 2020, pp. 577–591, doi:10.1007/s00162-020-00520-4.

Peitz, Sebastian, et al. “Data-Driven Model Predictive Control Using Interpolated Koopman  Generators.”10.1137/20M1325678__SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, 2020, pp. 2162–93, doi:10.1137/20M1325678.

Schumacher, Jan, and Sebastian Rezat. “Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge.”10.1007/978-3-662-61194-4_5__Zeichen und Sprache im Mathematikunterricht, edited by Gert Kadunz, Springer, 2020, doi:10.1007/978-3-662-61194-4_5.

Schumacher, Jan. Deduktion Und Abduktion Beim Diagrammatischen Schließen – Das Didaktische Potential Der Peirceschen Semiotik. LibreCat University, 2020, doi:10.17877/DE290R-21555.