Beermann, D., Dellnitz, M., Peitz, S., &; Volkwein, S. (2018). Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition. In Reduced-Order Modeling (ROM) for Simulation and Optimization (pp. 47–72). Cham.https://doi.org/10.1007/978-3-319-75319-5_3 

Klus, S., Gelß, P., Peitz, S., &; Schütte, C. (2018). Tensor-based dynamic mode decomposition. Nonlinearity, 31(7), 3359–3380.https://doi.org/10.1088/1361-6544/aabc8f 

Beermann, D., Dellnitz, M., Peitz, S., &; Volkwein, S. (2018). POD-based multiobjective optimal control of PDEs with non-smooth objectives. In PAMM (pp. 51–54).https://doi.org/10.1002/pamm.201710015 

Feldmann, N., Jurgelucks, B., Claes, L., &; Henning, B. (2018). Vollständige Charakterisierung von piezoelektrischen Scheiben mit Ringelektroden. Workshop “Messtechnische Anwendungen von Ultraschall”, Drübeck.

Gölz, C., Voelcker-Rehage, C., Mora, K., Reuter, E.-M., Godde, B., Dellnitz, M.,https://doi.org/10.3389/fphys.2018.01540__Vieluf, S. (2018). Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics. Frontiers in Physiology.https://doi.org/10.3389/fphys.2018.01540 

Vieluf, S., Mora, K., Gölz, C., Reuter, E.-M., Godde, B., Dellnitz, M.,https://doi.org/10.1016/j.neuroscience.2018.07.025__Voelcker-Rehage, C. (2018). Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control. Neuroscience, 203–213.https://doi.org/10.1016/j.neuroscience.2018.07.025 

Bittracher, A., Koltai, P., Klus, S., Banisch, R., Dellnitz, M., &; Schütte, C. (2018). Transition Manifolds of Complex Metastable Systems. Journal of Nonlinear Science, 28, 471–512.https://doi.org/10.1007/s00332-017-9415-0 

Peitz, S. (2018). Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data. ArXiv:1801.06419.

Klus, S., Peitz, S., &; Schuster, I. (2018). Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions. ArXiv:1805.10118.

Fiol, M. A., Mazzuoccolo, G., &; Steffen, E. (2018). Measures of Edge-Uncolorability of Cubic Graphs. The Electronic Journal of Combinatorics, 25(4).

Jin, L., Mazzuoccolo, G., &; Steffen, E. (2018). Cores, joins  and the Fano-flow conjectures. Discussiones Mathematicae Graph Theory, 38, 165–175.https://doi.org/10.7151/dmgt.1999 

Steffen, E. (2018). Approximating Vizing’s independence number conjecture. Australasian Journal of Combinatorics, 71(1), 153–160.

Nührenbörger, M., Bönig, D., Häsel-Weide, U., Korff, N., &; Scherer, P. (2018). Inklusiver Mathematikunterricht - vernetzt zwischen Mathematikdidaktik und Sonderpädagogik. In F. D. der M. der U. P. In: (Ed.), Beiträge zum Mathematikunterricht 2018. Vorträge zur Mathematikdidaktik und zur Schnittstelle Mathematik/Mathematikdidaktik auf der gemeinsamen Jahrestagung der GDM und DMV (pp. 103–104).

Moser-Opitz, E., Grob, U., Wittich, C., Häsel-Weide, U., &; Nührenbörger, M. (2018). Fostering the Computation Competence of Low Achievers through Cooperative Learning in Inclusive Classrooms: A Longitudinal Study. Learning Disabilities: A Contemporary Journal, 16, 19–35.

Häsel-Weide, U., Nührenbörger, M., &; Reinold, M. (2018). Förderkommentar Lernen zum Zahlenbuch 3. Klett.

Häsel-Weide, U., Nührenbörger, M., &; Reinold, M. (2018). Das Zahlenbuch 3. Förderheft. Klett.

Black, T. (2018). Global Very Weak Solutions to a Chemotaxis-Fluid System with Nonlinear Diffusion. SIAM Journal on Mathematical Analysis, 50(4), 4087–4116.https://doi.org/10.1137/17m1159488 

Black, T. (2018). Eventual smoothness of generalized solutions to a singular chemotaxis-Stokes system in 2D. Journal of Differential Equations, 265(5), 2296–2339.https://doi.org/10.1016/j.jde.2018.04.035 

Black, T. (2018). Global solvability of chemotaxis–fluid systems with nonlinear diffusion and matrix-valued sensitivities in three dimensions. Nonlinear Analysis, 180, 129–153.https://doi.org/10.1016/j.na.2018.10.003 

Peitz, S., Ober-Blöbaum, S., &; Dellnitz, M. (2018). Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling. Acta Applicandae Mathematicae, 161(1), 171–199.https://doi.org/10.1007/s10440-018-0209-7