D. Gläser. Dataset, (2021)Related to: Inga Berre, Wietse M. Boon, Bernd Flemisch, Alessio Fumagalli, Dennis Gläser, Eirik Keilegavlen, Anna Scotti, Ivar Stefansson, Alexandru Tatomir, Konstantin Brenner, Samuel Burbulla, Philippe Devloo, Omar Duran, Marco Favino, Julian Hennicker, I-Hsien Lee, Konstantin Lipnikov, Roland Masson, Klaus Mosthaf, Maria Giuseppina Chiara Nestola, Chuen-Fa Ni, Kirill Nikitin, Philipp Schädle, Daniil Svyatskiy, Ruslan Yanbarisov, Patrick Zulian, Verification benchmarks for single-phase flow in three-dimensional fractured porous media, Advances in Water Resources, Volume 147, 2021, 103759, ISSN 0309-1708. doi: 10.1016/j.advwatres.2020.103759.
S. Hermann. Dataset, (2022)Related to: Hermann, S., Fehr, J. Documenting research software in engineering science. Sci Rep 12, 6567 (2022). doi: 10.1038/s41598-022-10376-9.
D. Gläser. Dataset, (2020)Related to: I. Berre, W. Boon, B. Flemisch, A. Fumagalli, D. Gläser, E. Keilegavlen, A. Scotti, I. Stefansson, and A. Tatomir. Call for participation: Verification benchmarks for single-phase flow in three-dimensional fractured porous media, 2018. arXiv: 1809.06926.
M. Deimling, und M. Kirchhof. Dataset, (2021)Related to: Asymmetric Catalysis in Liquid Confinement: Probing the Performance of Novel Chiral Rhodium-Diene Complexes in Microemulsions and Conventional Solvents. M. Deimling, M. Kirchhof, B. Schwager, Y. Qawasmi, A. Savin, T. Mühlhäuser, W. Frey, B. Claasen, A. Baro, T. Sottmann, S. Laschat, Chem. Eur. J. 2019, 25, 9464. doi: 10.1002/chem.201900947.
M. Kirchhof. Dataset, (2021)Related to: M. Kirchhof, K. Gugeler, F. R. Fischer, M. Nowakowski, A. Bauer, S. Alvarez-Barcia, K. Abitaev, M. Schnierle, Y. Qawasmi, W. Frey, A. Baro, D. P. Estes, T. Sottmann, M. R. Ringenberg, B. Plietker, M. Bauer, J. Kästner, S. Laschat, Organometallics 2020, 39, 3131-3145. doi: 10.1021/acs.organomet.0c00310.
A. Mansour, C. Kaltenbach, und E. Laurien. High performance computing in science and engineering '16 : transactions of the High Performance Computing Center, Stuttgart (HLRS) 2016, Seite 511-528. Cham, Springer, (2016)
S. Joas, W. Essig, F. Fröhlich, und M. Kreutzbruck. Proceedings of the Europe/Africa Conference Dresden 2017 - Polymer Processing Society PPS, 2055, Seite 120003. Melville, New York, AIP Publishing, (2019)
M. Mikusz, D. Heber, C. Katzfuß, M. Monaumi, und T. Tauterat. Research and Innovation in Manufacturing : Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems, 41, Red Hook, NY, Curran, (2015)
M. Schneider, und M. Liewald. Proceedings of 21st International ESAFORM Conference on Material Forming (ESAFORM 2018), 1960, Seite 150012. Melville, NY, AIP Publishing, (2018)
K. Munoz Barón, K. Sharma, M. Nitzsche, P. Ziegler, D. Koch, und I. Kallfass. PCIM Europe digital days 2020 : International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, Seite 1344-1350. Frankfurt am Main, VDE Verlag, (2020)
A. Majee, M. Bier, R. Blossey, und R. Podgornik. Physical Review. E, Covering Statistical, Nonlinear, Biological, and Soft Matter Physics, 100 (5):
050601(2019)
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.
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.
D. Holzmüller, V. Zaverkin, J. Kästner, und I. Steinwart. Software, (2023)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2023. arXiv: 2203.09410.
D. Holzmüller, L. Grinsztajn, und I. Steinwart. Software, (2024)Related to: David Holzmüller, Léo Grinsztajn, and Ingo Steinwart. Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data, 2024. arXiv: 2407.04491.
S. Reuschen, T. Xu, und W. Nowak. Dataset, (2020)Related to: Reuschen, S., Xu, T., Nowak, W., 2020. Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC. Advances in Water Resources 141, 103614. doi: 10.1016/j.advwatres.2020.103614.
L. Scholz, und C. Bringedal. Dataset, (2021)Related to: Scholz, L., Bringedal, C. A Three-Dimensional Homogenization Approach for Effective Heat Transport in Thin Porous Media. Transp Porous Med (2022). doi: 10.1007/s11242-022-01746-y.
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".
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.
L. Keim, H. Class, L. Schirmer, B. Strauch, K. Wendel, und M. Zimmer. Dataset, (2023)Related to: Class, H.; Keim, L.; Schirmer, L.; Strauch, B.; Wendel, K.; Zimmer, M. Seasonal Dynamics of Gaseous CO2 Concentrations in a Karst Cave Correspond with Aqueous Concentrations in a Stagnant Water Column. Geosciences 2023, 13, 51. doi: 10.3390/geosciences13020051.
L. Kloker, und C. Bringedal. Dataset, (2022)Related to: Leon H. Kloker and Carina Bringedal, Solution approaches for evaporation-driven density instabilities in a slab of saturated porous media, Physics of Fluids 34, 096606 (2022). doi: 10.1063/5.0110129.
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.