L. Nölle, P. Lerge, O. Martynenko, I. Wochner, F. Kempter, C. Kleinbach, S. Schmitt, and J. Fehr. Dataset, (2022)Related to: Kleinbach, C., Martynenko, O., Promies, J., Haeufle, D.F., Fehr, J., Schmitt, S., 2017. Implementation and validation of the extended hill-type muscle model with robust routing capabilities in LS-DYNA for active human body models. Biomedical engineering online 16, 109. doi: 10.1186/s12938-017-0399-7.
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. Dataset, (2022)Related to: Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks. arXiv: 2210.07182.
G. Tkachev. Software, (2021)Related to: G. Tkachev, S. Frey and T. Ertl, "S4: Self-Supervised learning of Spatiotemporal Similarity," in IEEE Transactions on Visualization and Computer Graphics. doi: 10.1109/TVCG.2021.3101418.
F. Bechler. Dataset, (2024)Related to: Related Publication: Bechler, F.: Enabling Holistic Vehicle Safety - Combined Knowledge and Information through a Graph-based Approach, Dissertation, University of Stuttgart, Shaker Verlag, Aachen, tbd.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
S. Schulz, C. Bringedal, and S. Ackermann. Dataset, (2021)Related to: SimTech Project work "Herleitung reduzierter Modelle einer Zweiphasenströmung zwischen parallelen Platten mit Slip-Bedingungen".
R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr. (2024), "Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems". arXiv: 2405.10465.
R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, T. Wenzel, B. Haasdonk, P. Toktaliev, O. Iliev (2024), "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". arXiv: 2405.19170.
J. Pelzer. Software, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
M. Alkämper, and J. Magiera. Software, (2022)Related to: M. Alkämper, J. M. Magiera and C. Rohde, “An Interface Preserving Moving Mesh in Multiple Space Dimensions” (2021), submitted. arXiv: 2112.11956.
T. Praditia. Dataset, (2020)Related to: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W.: Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies 2020.
P. Stärk, and A. Schlaich. Software, (2024)Related to: Artemov, V.; Frank, L.; Doronin, R.; Stärk, P.; Schlaich, A.; Andreev, A.; Leisner, T.; Radenovic, A.; Kiselev, A. The Three-Phase Contact Potential Difference Modulates the Water Surface Charge. J. Phys. Chem. Lett. 2023, 14, 4796-4802. doi: 10.1021/acs.jpclett.3c00479.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
A. Schlaich. Dataset, (2021)Related to: Schlaich, A., Jin, D., Bocquet, L. & Coasne, B. (2021). Wetting transition of ionic liquids at metal surfaces: A computational molecular approach to electronic screening using a virtual Thomas-Fermi fluid. arXiv: 2002.11526.
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. Dataset, (2022)Related to: Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.
P. Santana Chacon, M. Hammer, I. Wochner, J. Walter, and 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. Homs Pons, and R. Lautenschlager. Software, (2024)Related to: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models, submitted to GAMM Mitteilungen.
Please include the acknowledgement in publications for those who are funded by our Cluster of Excellence:
Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016
