S. Vahid Dastjerdi, H. Steeb, M. Ruf, D. Lee, F. Weinhardt, N. Karadimitriou, und H. Class. Dataset, (2021)Related to: Weinhart, F., Class, H., Vahid Dastjerdi, S., Karadimitriou, N., Lee, D., & Steeb, H. (2021). Experimental Methods and Imaging for Enzymatically Induced Calcite Precipitation in a microfluidic cell. Water Resources Research, Technical Reports: Methods. doi: 10.1029/2020WR029361.
F. Weinhardt, H. Class, S. Vahid Dastjerdi, N. Karadimitriou, D. Lee, und H. Steeb. Dataset, (2021)Related to: Weinhardt, F.; Class, H.; Vahid Dastjerdi, S.; Karadimitriou, N.; Lee, D.; Steeb, H.(2021). Experimental Methods and Imaging for Enzymatically Induced Calcite Precipitation in a microfluidic cell. Accepted in Water Resources Research. doi: 10.1029/2020WR029361.
D. Fauser, M. Kuhn, und H. Steeb. Dataset, (2021)Related to: Fauser, Dominik; Steeb, Holger, (2021), "Influence of Humidity on the Rheology of Thermoresponsive Shape Memory Polymers", Journal of Materials Science (under review).
M. Balcewicz, M. Ruf, H. Steeb, und E. Saenger. Dataset, (2021)Related to: Balcewicz, M., Siegert, M., Gurris, M., Ruf, M., Krach, D., Steeb, H., & Saenger, E.H. (2021). Digital rock physics: A geological driven workflow for the segmentation of anisotropic Ruhr sandstone. Frontiers in Earth Science (under review).
M. Ruf, M. Balcewicz, E. Saenger, und H. Steeb. Dataset, (2021)Related to: Balcewicz, M., Siegert, M., Gurris, M., Ruf, M., Krach, D., Steeb, H., & Saenger, E.H. (2021). Digital rock physics: A geological driven workflow for the segmentation of anisotropic Ruhr sandstone. Frontiers in Earth Science (under review).
D. Fauser, und H. Steeb. Dataset, (2021)Related to: Fauser, Dominik; Steeb, Holger, (2021), "Influence of Humidity on the Rheology of Thermoresponsive Shape Memory Polymers", Journal of Materials Science (under review).
D. Holzmüller, V. Zaverkin, J. Kästner, und 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.
M. Ruf, und H. Steeb. Dataset, (2020)Related to: Ruf, M., & Steeb, H. (2020). An open, modular, and flexible micro X-ray computed tomography system for research. Review of Scientific Instruments, 91(11), 113102. doi: 10.1063/5.0019541.
D. Holzmüller. Software, (2022)Related to: David Holzmüller and Dirk Pflüger. Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. Sparse Grids and Applications - Munich 2018 (2021). doi: 10.1007/978-3-030-81362-8_4.
M. Ruf, und H. Steeb. Dataset, (2020)Related to: Ruf, M., & Steeb, H. (2020). An open, modular, and flexible micro X-ray computed tomography system for research. Review of Scientific Instruments, 91(11), 113102. doi: 10.1063/5.0019541.
M. Balcewicz, M. Ruf, H. Steeb, und E. Saenger. Dataset, (2021)Related to: Balcewicz, M., Siegert, M., Gurris, M., Ruf, M., Krach, D., Steeb, H., & Saenger, E.H. (2021). Digital rock physics: A geological driven workflow for the segmentation of anisotropic Ruhr sandstone. Frontiers in Earth Science (under review).
A. Gonzalez-Nicolas Alvarez. Software, (2021)Related to: Gonzalez-Nicolas, A.; Schwientek, M.; Sinsbeck, M; Nowak, W. Characterization of export regimes in concentration-discharge plots via an advanced time-series model and event-based sampling strategies. Water 2021, 13, 1723. doi: 10.3390/w13131723.
A. Gonzalez-Nicolas Alvarez. Dataset, (2021)Related to: Gonzalez-Nicolas, A.; Schwientek, M.; Sinsbeck, M; Nowak, W. Characterization of export regimes in concentration-discharge plots via an advanced time-series model and event-based sampling strategies. Water 2021, 13, 1723. doi: 10.3390/w13131723.
J. Kneifl, und J. Fehr. Software, (2020)Related to: Jonas Kneifl, Dennis Grunert, and Joerg Fehr (2021). A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning. In: International Journal for Numerical Methods in Engineering, 122:4774-4786. doi: 10.1002/nme.6712.
D. Holzmüller. Software, (2021)Related to: David Holzmüller. On the Universality of the Double Descent Peak in Ridgeless Regression. International Conference on Learning Representations, 2021. arXiv: 2010.01851.
T. Munz, D. Väth, P. Kuznecov, N. Vu, und D. Weiskopf. Dataset, (2021)Related to: T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visual-Interactive Neural Machine Translation". Graphics Interface. 2021.
S. Schulz, C. Bringedal, und S. Ackermann. Dataset, (2021)Related to: SimTech Project work "Herleitung reduzierter Modelle einer Zweiphasenströmung zwischen parallelen Platten mit Slip-Bedingungen".
M. Alkämper, und 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. Munz, D. Väth, P. Kuznecov, N. Vu, und D. Weiskopf. Software, (2021)Related to: T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visual-Interactive Neural Machine Translation". Graphics Interface. 2021.
T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, und D. Weiskopf. Software, (2020)Related to: T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf. "Comparative Visual Gaze Analysis for Virtual Board Games". Proceedings of the 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020). 2020. DOI: 10.1145/3430036.3430038.
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.
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.
V. Zaverkin, D. Holzmüller, I. Steinwart, und J. Kästner. Software, (2021)Related to: V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021). doi: 10.1021/acs.jctc.1c00527.
T. Munz, D. Väth, P. Kuznecov, N. Vu, und D. Weiskopf. Software, (2022)Related to: T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visualization-based improvement of neural machine translation", Computers & Graphics, 2021. doi: 10.1016/j.cag.2021.12.003.
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.
H. Hsueh. Dataset, (2021)Related to: Han-Fang Hsueh, Anneli Guthke, Thomas Wöhling, Wolfgang Nowak: Diagnosis of model-structural errors with a sliding time-window Bayesian analysis. In: Water Resource Research (submitted). arXiv: 2107.09399.
T. Munz, R. Garcia, und D. Weiskopf. Software, (2021)Related to: R. Garcia, T. Munz, and D. Weiskopf. "Visual Analytics Tool for the Interpretation of Hidden States in Recurrent Neural Networks". Visual Computing for Industry, Biomedicine, and Art (VCIBA). 2021. doi: 10.1186/s42492-021-00090-0.
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.