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. 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).
I. Banerjee. Dataset, (2021)Related to: Banerjee, I., Guthke, A., Van de Ven, C. J. C., Mumford, K. G. & Nowak, W. (2021). Overcoming the Model-Data-Fit Problem in Porous Media: A Quantitative Method to Compare Invasion-Percolation Models to High-Resolution Data. Water Resources Research, 57, e2021WR029986. doi: 10.1029/2021WR029986.
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).
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. Fauser, und H. Steeb. Dataset, (2021)Related to: Fauser, Dominik; Steeb, Holger (2022) "Influence of humidity on the rheology of thermoresponsive shape memory polymers", Journal of Materials Science 57, 9508-9524. doi: 10.1007/s10853-022-07206-8.
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.
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.
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.
M. Kelm, C. Bringedal, und B. Flemisch. Dataset, (2023)Related to: Kelm, M., Gärttner, S., Bringedal, C. et al. Comparison study of phase-field and level-set method for three-phase systems including two minerals. Comput Geosci 26, 545-570 (2022). doi: 10.1007/s10596-022-10142-w.
J. Kneifl, J. Rettberg, und J. Herb. Software, (2024)Related to: Johannes Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, Jörg Fehr, and Bernard Haasdonk. Data-driven identification of latent port-Hamiltonian systems. Arxiv, 2024. arXiv: 2408.08185.
J. Kneifl, D. Rosin, O. Avci, O. Röhrle, und J. Fehr. Software, (2023)Related to: Kneifl, J, Rosin, D., Röhrle, O., Fehr, J.: Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction, 2022. tbd.
L. Nölle, P. Lerge, O. Martynenko, I. Wochner, F. Kempter, C. Kleinbach, S. Schmitt, und 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.
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.
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.
T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. Butz, und W. Nowak. Dataset, (2022)Related to: Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S., Butz, M.V., Nowak, W.: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. Earth and Space Science Open Archive (2022). doi: 10.1002/essoar.10511934.1.