L. Skoury, und T. Wortmann. Dataset, (2024)Related to: L. Skoury et al., “Towards data-informed co-design in digital fabrication”, Autom. Constr., vol. 158, p. 105229, Feb. 2024. doi: 10.1016/j.autcon.2023.105229.
K. Taghizadeh, M. Ruf, S. Luding, und H. Steeb. Proceedings of the National Academy of Sciences of the United States of America, 120 (26):
e2219999120(2023)
J. Rettberg, D. Wittwar, und R. Herkert. Software, (2023)Related to: Rettberg, J.; Wittwar, D.; Buchfink, P.; Brauchler, A.; Ziegler, P.; Fehr, J.; Haasdonk, B.: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. Mathematical and Computer Modelling of Dynamical Systems, 2023, Vol. 29, No. 1, 116-148. doi: 10.1080/13873954.2023.2173238.
I. Tischler, A. Schlaich, und C. Holm. Software, (2023)Related to: Ingo Tischler, Alexander Schlaich, Christian Holm. Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement. ACS Omega 2023. doi: 10.1021/acsomega.3c06195.
P. Santana Chacon, M. Hammer, I. Wochner, J. Walter, und S. Schmitt. Software, (2023)Related to: P. F. S. Chacon, M. Hammer, I. Wochner, J. R. Walter and S. Schmitt. A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers. doi: 10.1080/10255842.2023.2293652.
C. Lohrmann, und C. Holm. Software, (2023)Related to: Lohrmann, Holm, Datta: Influence of bacterial motility and hydrodynamics on phage bacteria encounters, to be submitted.
R. Pesl, M. Stötzner, I. Georgievski, und M. Aiello. Dataset, (2023)Related to: Pesl, R.D., Stötzner, M., Georgievski, I., Aiello, M.: Uncovering LLMs for Service-Composition: Challenges and Opportunities. In: ICSOC 2023 Workshops (2023).
L. Werneck, E. Yildiz, M. Han, M. Keip, M. Sitti, und M. Ortiz. Software, (2023)Related to: Werneck, L., Han, M., Yildiz, E., Keip, M.-A., Sitti, M., & Ortiz, M. (2023). A Simple Quantitative Model of Neuromodulation, Part I: Ion Flow Through Neural Ion Channels. Journal of the Mechanics and Physics of Solids, 182:105457. doi: 10.1016/j.jmps.2023.105457.
J. Kneifl, und J. Fehr. Software, (2023)Related to: Kneifl, J., Kutz, J. N., Brunton, S.L., Fehr, J.: Multi-Hierarchical Surrogate Learning of Structural Dynamical Systems Using Graph Convolutional Neural Networks. To be submitted (2023).