Lichtenstein T, Mamiyev Z, Braun C, et al. Probing quasi-one-dimensional band structures by plasmon spectroscopy. Physical Review B. 2018;97(16). doi:10.1103/physrevb.97.165421

Camberg AA, Bohner F, Tölle J, Schneidt A, Meiners S, Tröster T. Formability enhancement of EN AW-518210.1088/1757-899x/418/1/012018__aluminum alloy sheet metal parts in a flash forming process: testing, calibration and evaluation of fracture models. IOP Conference Series: Materials Science and Engineering. 2018. doi:10.1088/1757-899x/418/1/012018

Ju X, Mahnken R. Goal-oriented adaptivity for linear elastic micromorphic continua based on primal and adjoint consistency analysis. International Journal for Numerical Methods in Engineering. 2018:472-473. doi:10.1002/nme.5996

Caylak I, Dridger A, Mahnken R. “Stochastic hyperelastic modeling considering dependency of material parameters.” Computational Mechanics. 2018;62(6):1273-1285.

Nicholson CW, Lücke A, Schmidt WG, et al. Beyond the molecular movie: Dynamics of bands and bonds during a photoinduced phase transition. Science. 2018:821-825. doi:10.1126/science.aar4183

Paszkiewicz M, Biktagirov T, Aldahhak H, et al. Unraveling the Oxidation and Spin State of Mn–Corrole through X-ray Spectroscopy and Quantum Chemical Analysis. The Journal of Physical Chemistry Letters. 2018:6412-6420. doi:10.1021/acs.jpclett.8b02525

Aldahhak H, Paszkiewicz M, Rauls E, et al. Identifying On-Surface Site-Selective Chemical Conversions by Theory-Aided NEXAFS Spectroscopy: The Case of Free-Base Corroles on Ag(111). Chemistry - A European Journal. 2018:6787-6797. doi:10.1002/chem.201705921

Ju X, Mahnken R. eGoal-oriented adaptivity on mean-field and full-field homogenization methods with a view to hierarchical unit cells“. In: 31st International Workshop on Research in Mechnanics of Composite. ; 2018.

Dridger A, Caylak I, Mahnken R, Penner E. eOn the connection between possibility theory and probability box theory in structural mechanics“. In: 13th World Congress in Computational Mechanics . ; 2018.

Lenz P, M R. eMean-field homogenization of multi-layered thermo-chemo-elastic composites including damage“. In: 31st International Workshop on Research in Mechnanics of Composite. ; 2018.

Penner E, Caylak I, Dridger A, Mahnken R. “Possibilistic and stochastic analysis using for rubber-like materials.”10.1002/pamm.201800153__PAMM. 2018. doi:10.1002/pamm.201800153

Caylak I, Penner E, Dridger A, Mahnken R. “A fuzzy-stochastic model for transversely fiber reinforced plastics.”10.1002/pamm.201800121__PAMM. 2018. doi:10.1002/pamm.201800121

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

Rollová E, Schubert M, Steffen E. Flows in Signed Graphs with Two Negative Edges . The Electronic Journal of Combinatorics. 2018;25(2). doi:10.37236/4458

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

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

Ahmadi Fahandar M, Hüllermeier E. Learning to Rank Based on Analogical Reasoning. In: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI). ; 2018:2951-2958.

El Mesaoudi-Paul A, Hüllermeier E, Busa-Fekete R. Ranking Distributions based on Noisy Sorting. In: Proc. 35th Int. Conference on Machine Learning (ICML). Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn. Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn; 2018:3469-3477.

Hesse M, Timmermann J, Hüllermeier E, Trächtler A. A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. In: Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24. ; 2018:15-20.

Mencia EL, Fürnkranz J, Hüllermeier E, Rapp M. Learning interpretable rules for multi-label classification. In: Jair Escalante H, Escalera S, Guyon I, et al., eds. Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer; 2018:81-113.