Consultation Phases in Mathematics Learning and Support Centres
M. Schürmann, A. Panse, Z. Shaikh, R. Biehler, N. Schaper, M. Liebendörfer, J. Hilgert, International Journal of Research in Undergraduate Mathematics Education 8 (2021) 94–120.

The process of actively building a model for a randomization test – insights into learners’ modeling activities based on a case study
S. Podworny, R. Biehler, Mathematical Thinking and Learning 24 (2021) 291–311.

A first introduction to data science education in secondary schools: Teaching and learning about data exploration with<scp>CODAP</scp>using survey data
D. Frischemeier, R. Biehler, S. Podworny, L. Budde, Teaching Statistics 43 (2021) 2–9.

Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation
R.I. McLachlan, C. Offen, Foundations of Computational Mathematics 20 (2020) 1363–1400.

Detection of high codimensional bifurcations in variational PDEs
L.M. Kreusser, R.I. McLachlan, C. Offen, Nonlinearity 33 (2020) 2335–2363.

Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach
K. Flaßkamp, S. Ober-Blöbaum, S. Peitz, in: O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation, Springer, Cham, 2020.

Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator
S. Klus, F. Nüske, B. Hamzi, Entropy (2020).

Das Kompetenzzentrum Hochschuldidaktik Mathematik (khdm)
R. Hochmuth, M. Liebendörfer, R. Biehler, A. Eichler, Neues Handbuch Hochschullehre 95 (2020) 117–138.

Pareto Explorer: a global/local exploration tool for many-objective optimization problems
O. Schütze, O. Cuate, A. Martín, S. Peitz, M. Dellnitz, Engineering Optimization 52 (2020) 832–855.

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, C. Schütte, Physica D: Nonlinear Phenomena 406 (2020).