S. Ober-Blöbaum and S. Peitz, “Explicit multiobjective model predictive control for nonlinear systems  with symmetries,”10.1002/rnc.5281__International Journal of Robust and Nonlinear Control, vol. 31(2), pp. 380–403, 2021, doi:10.1002/rnc.5281 .

W. Djema, L. Giraldi, S. Maslovskaya, and O. Bernard, “Turnpike features in optimal selection of species represented by quota models,”10.1016/j.automatica.2021.109804__Automatica, vol. 132, Art. no. 109804, 2021, doi:10.1016/j.automatica.2021.109804 .

J. Li, Y. Ma, Y. Shi, W. Wang, and Y. Wu, “On 3-flow-critical graphs,”10.1016/j.ejc.2021.103451__European Journal of Combinatorics, vol. 100, Art. no. 103451, 2021, doi:10.1016/j.ejc.2021.103451 .

J. Klüners and T. Komatsu, “Imaginary multiquadratic number fields with class group of exponent $ and $,”10.1090/mcom/3609__Mathematics of Computation, vol. 90, no. 329, pp. 1483–1497, 2021, doi:10.1090/mcom/3609 .

M. Kirschmer, F. Narbonne, C. Ritzenthaler, and D. Robert, “Spanning the isogeny class of a power of an elliptic curve,”10.1090/mcom/3672__Mathematics of Computation, vol. 91, no. 333, pp. 401–449, 2021, doi:10.1090/mcom/3672 .

C. Offen and S. Ober-Blöbaum, “Bifurcation preserving discretisations of optimal control problems,”https://doi.org/10.1016/j.ifacol.2021.11.099__vol. 54(19). pp. 334–339, 2021, doi:https://doi.org/10.1016/j.ifacol.2021.11.099 .

S. Ridderbusch, C. Offen, S. Ober-Blöbaum, and P. Goulart, “Learning ODE Models with Qualitative Structure Using Gaussian Processes ,”10.1109/CDC45484.2021.9683426__in 2021 60th IEEE Conference on Decision and Control (CDC), Austin, TX, USA, 2021, p. 2896, doi:10.1109/CDC45484.2021.9683426 .

N. Vertovec, S. Ober-Blöbaum, and K. Margellos, “Multi-objective minimum time optimal control for low-thrust trajectory design,” Rotterdam, the Netherlands, pp. 1975–1980.

F. Jiménez and S. Ober-Blöbaum, “Fractional Damping Through Restricted Calculus of Variations,” in Nichtlineare Sci 31, 2021, vol. 46.

T. Binz and B. Kovács, “A convergent finite element algorithm for mean curvature flow in higher codimension,” arXiv, 2021.

T. Binz and B. Kovács, “A convergent finite element algorithm for generalized mean curvature flows of closed surfaces,”10.1093/imanum/drab043__IMA Journal of Numerical Analysis, vol. 42, no. 3, pp. 2545–2588, 2021, doi:10.1093/imanum/drab043 .

P. Harder and B. Kovács, “Error estimates for the Cahn–Hilliard equation with dynamic boundary conditions,”10.1093/imanum/drab045__IMA Journal of Numerical Analysis, vol. 42, no. 3, pp. 2589–2620, 2021, doi:10.1093/imanum/drab045 .

J. Nick, B. Kovács, and C. Lubich, “Correction to: Stable and convergent fully discrete interior–exterior coupling of Maxwell’s equations,”10.1007/s00211-021-01196-6__Numerische Mathematik, vol. 147, no. 4, pp. 997–1000, 2021, doi:10.1007/s00211-021-01196-6 .

B. Kovács, B. Li, and C. Lubich, “A convergent evolving finite element algorithm for Willmore flow of closed surfaces,”10.1007/s00211-021-01238-z__Numerische Mathematik, vol. 149, no. 3, pp. 595–643, 2021, doi:10.1007/s00211-021-01238-z .

R. I. McLachlan and C. Offen, “Preservation of Bifurcations of Hamiltonian Boundary Value Problems Under Discretisation,” Foundations of Computational Mathematics, vol. 20, no. 6, pp. 1363–1400, 2020.

L. M. Kreusser, R. I. McLachlan, and C. Offen, “Detection of high codimensional bifurcations in variational PDEs,” Nonlinearity, vol. 33, no. 5, pp. 2335–2363, 2020.

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

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

O. Schütze, O. Cuate, A. Martín, S. Peitz, and M. Dellnitz, “Pareto Explorer: a global/local exploration tool for many-objective optimization problems,” Engineering Optimization, vol. 52, no. 5, pp. 832–855, 2020.

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