R. Skukies. Software, (2023)Related to: Skukies, R., & Ehinger, B. V. (2023). The effect of estimation time window length on overlap correction in EEG data (2023.06.05.543689). bioRxiv. doi: 10.1101/2023.06.05.543689.
P. Kurzeja, and H. Steeb. Philosophical transactions of the Royal Society. Series A, Mathematical, physical and engineering sciences, 380 (2237):
20210370(2022)
J. Kneifl, J. Rettberg, and 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.
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. 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.
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.
D. Holzmüller, V. Zaverkin, J. Kästner, and 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, and 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. Schulz, C. Bringedal, and 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, and 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.
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.
J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and 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.
J. Kneifl, J. Rettberg, and J. Fehr. Software, (2024)Related to: Rettberg, J., Kneifl, J., Herb, J., Buchfink, P., Fehr, J., Haasdonk, B.: Data-driven identification of low-dimensional port-Hamiltonian systems. To be submitted (2024).
J. Kneifl, and 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).
S. Vahid Dastjerdi, N. Karadimitriou, and H. Steeb. Dataset, (2022)Related to: Vahid Dastjerdi, S.; Karadimitriou, N.; Hassanizadeh, S. M. & Steeb, H.: Experimental evaluation of fluid connectivity in two-phase flow in porous media. Advances in Water Resources 172 (2023), 104378. doi: 10.1016/j.advwatres.2023.104378.
T. Giess, S. Itzigehl, J. Range, J. Bruckner, and J. Pleiss. Dataset, (2022)Related to: Giess T., Itzigehl S., Range J. P., Bruckner J. R., Pleiss J., FAIR and scalable management of small-angle X-ray scattering data, 2023. doi: 10.1107/S1600576723001577.
P. Tilli. Dataset, (2024)Related to: Esra Dönmez*, Pascal Tilli*, Hsiu-Yu Yang*, Ngoc Thang Vu, and Carina Silberer. 2023. HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 364-388, Singapore. Association for Computational Linguistics. doi: 10.18653/v1/2023.conll-1.24.
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.
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.
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.
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.
J. Pelzer. Dataset, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Scalable Machine Learning Approach for Heat Transport in Groundwater". In proceedings of 23rd International Conference of Machine Learning and Applications (2024). In progress.
A. Baier, and D. Frank. Software, (2023)Related to: Baier, Alexandra, Boukhers, Zeyd, & Staab, Steffen (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. ArXiv, abs/2103.06727. arXiv: abs/2103.06727.
J. Kneifl, and 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.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Cristancho, D., Delgado, D., Martínez, F., Abolghassemi Fakhree, M. A., & Jouyban, A. (2011). Volumetric properties of glycerol + water mixtures at several temperatures and correlation with the Jouyban-Acree model. Revista Colombiana de Ciencias Químico Farmacéuticas, 40. 92-115.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Mikhail, S. Z., & Kimel, W. R. (1961). Densities and Viscosities of Methanol-Water Mixtures. Journal of Chemical & Engineering Data, 6(4), 533-537. doi: 10.1021/je60011a015.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Gültig, M., Range, J. P., Schmitz, B., & Pleiss, J. (2022). Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML. Journal of Chemical & Engineering Data,. doi: 10.1021/acs.jced.2c00391.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Gültig, M., Range, J. P., Schmitz, B., & Pleiss, J. (2022). Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML. Journal of Chemical & Engineering Data,. doi: 10.1021/acs.jced.2c00391.
T. Siebert, N. Stutzig, J. Fehr, C. Holzapfel, S. Hunger, L. Broß, and M. Millard. dvs-Biomechanik 2023 Tagungsband : Beiträge zur Tagung der dvs-Sektion Biomechanik 2023 an der Otto-von-Guericke-Universität Magdeburg, page 180-182. Stuttgart, Steinbeis-Edition, (2023)
L. Schepp, B. Ahrens, M. Balcewicz, M. Duda, M. Nehler, M. Osorno, D. Uribe, H. Steeb, B. Nigon, F. Stöckhert and 5 other author(s). Dataset, (2020)Related to: Schepp, L.L., Ahrens, B., Balcewicz, M., Duda, M., Nehler, M., Osorno, M., Uribe, D., Steeb, S., Nigon, B., Stöckhert, F., Swanson, D.A., Siegert, M., Gurris, M. & Saenger, E.H. (2020). Digital rock physics and laboratory considerations on a high-porosity volcanic rock. Scientific Reports, 10, 5840. doi: 10.1038/s41598-020-62741-1.
M. Ruf, M. Balcewicz, E. Saenger, and 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).
M. Balcewicz, M. Ruf, H. Steeb, and 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, and 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, and H. Steeb. Dataset, (2021)Related to: Ruf, M., & Steeb, H. (2022). Effects of thermal treatment on acoustic waves in Carrara marble. International Journal of Rock Mechanics and Mining Sciences, 159, 105205. doi: 10.1016/j.ijrmms.2022.105205.