H. Jäger. Software, (2023)Related to: Jäger, Henrik, Alexander Schlaich, Jie Yang, Cheng Lian, Svyatoslav Kondrat und Christian Holm. 2023. A screening of results on the decay length in concentrated electrolytes. Faraday Discussions. Faraday Discussions (Februar). doi: 10.1039/d3fd00043e.
I. Tischler, A. Schlaich, and C. Holm. Software, (2023)Related to: Ingo Tischler, Alexander Schlaich, Christian Holm. Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement. ACS Omega 2023. doi: 10.1021/acsomega.3c06195.
S. Gravelle, D. Beyer, M. Brito, A. Schlaich, and C. Holm. Software, (2023)Related to: Simon Gravelle, David Beyer, Mariano Brito, Alexander Schlaich, Christian Holm: Reconstruction of NMR Relaxation Rates from Coarse-Grained Polymer Simulations, ChemRxiv, 2022. Preprint. doi: 10.26434/chemrxiv-2022-f90tv-v2.
S. Gravelle, C. Holm, and A. Schlaich. Software, (2023)Related to: Simon Gravelle, Sabina Haber-Pohlmeier, Carlos Mattea, Siegfried Stapf, Christian Holm and Alexander Schlaich: NMR Investigation of Water in Salt Crusts: Insights from Experiments and Molecular Simulations, ChemRxiv, 2023. doi: 10.26434/chemrxiv-2023-6dml7.
A. Schlaich. Software, (2024)Related to: Alexander Schlaich, Matthieu Vandamme, Marie Plazanet, Benoit Coasne, "Bridging Microscopic Dynamics and Hydraulic Permeability in Mechanically-Deformed Nanoporous Materials", (2024). arXiv: arXiv:2403.19812.
L. Werneck, E. Yildiz, M. Han, M. Keip, M. Sitti, and M. Ortiz. Software, (2023)Related to: Werneck, L., Han, M., Yildiz, E., Keip, M.-A., Sitti, M., & Ortiz, M. (2023). A Simple Quantitative Model of Neuromodulation, Part I: Ion Flow Through Neural Ion Channels. Journal of the Mechanics and Physics of Solids, 182:105457. doi: 10.1016/j.jmps.2023.105457.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: González, B., Calvar, N., Gómez, E., & Domínguez, Á. (2007). Density, dynamic viscosity, and derived properties of binary mixtures of methanol or ethanol with water, ethyl acetate, and methyl acetate at T=(293.15, 298.15, and 303.15)K. The Journal of Chemical Thermodynamics, 39(12), 1578-1588. doi: 10.1016/j.jct.2007.05.004.
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.
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.
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.
I. Tischler, C. Holm, and A. Schlaich. Software, (2022)Related to: Tischler, I., Schlaich, A., Holm, C., The Presence of a Wall Enhances the Probability for Ring-Closing Metathesis: Insights from Classical Polymer Theory and Atomistic Simulations. Macromol. Theory Simul. 2021, 30, 2000076. doi: 10.1002/mats.202000076.
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.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Segur, J. B., & Oberstar, H. E. (1951). Viscosity of Glycerol and Its Aqueous Solutions. Industrial & Engineering Chemistry, 43(9), 2117-2120. doi: 10.1021/ie50501a040.
R. Herkert. Software, (2023)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr: Randomized Symplectic Model Order Reduction for Hamiltonian Systemsm 2023. arXiv: 2303.04036.
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. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: D’Errico, G., Ortona, O., Capuano, F., & Vitagliano, V. (2004). Diffusion Coefficients for the Binary System Glycerol + Water at 25 °C. A Velocity Correlation Study. Journal of Chemical & Engineering Data, 49(6), 1665-1670. doi: 10.1021/je049917u.
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.
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.
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.
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.
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.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Derlacki, Z. J., Easteal, A. J., Edge, A. V. J., Woolf, L. A., & Roksandic, Z. (1985). Diffusion coefficients of methanol and water and the mutual diffusion coefficient in methanol-water solutions at 278 and 298 K. The Journal of Physical Chemistry, 89(24), 5318-5322. doi: 10.1021/j100270a039.
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.
J. Rettberg, D. Wittwar, and R. Herkert. Software, (2023)Related to: Rettberg, J.; Wittwar, D.; Buchfink, P.; Brauchler, A.; Ziegler, P.; Fehr, J.; Haasdonk, B.: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. Mathematical and Computer Modelling of Dynamical Systems, 2023, Vol. 29, No. 1, 116-148. doi: 10.1080/13873954.2023.2173238.
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.
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.
P. Rodegast, S. Maier, J. Kneifl, and J. Fehr. Software, (2023)Related to: Rodegast, P., Maier, S., Kneifl, J., Fehr, J.: On using Machine Learning Algorithms for Motorcycle Collision Detection, 2023. tbd.
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).
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.
T. Munz, D. Väth, P. Kuznecov, N. Vu, and 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.
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, 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.
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.
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.
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.
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.
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.
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.
I. Wochner, and S. Schmitt. Software, (2022)Related to: Wochner, I., Schumacher, P., Martius, G., Büchler, D., Schmitt, S., & Haeufle, D. F. (2022). Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks. Conference on Robot Learning (CoRL) 2022. url: https://openreview.net/forum?id=Xo3eOibXCQ8.
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.
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.
S. Gravelle, C. Holm, and A. Schlaich. Software, (2023)Related to: Simon Gravelle, Sabina Haber-Pohlmeier, Carlos Mattea, Siegfried Stapf, Christian Holm and Alexander Schlaich: NMR Investigation of Water in Salt Crusts: Insights from Experiments and Molecular Simulations, ChemRxiv, 2023. doi: 10.26434/chemrxiv-2023-6dml7.
M. Ruf, and H. Steeb. Dataset, (2023)Related to: Ruf, M., Lee, D., & Steeb, H. (2023). A multifunctional mechanical testing stage for micro X-ray computed tomography. Review of Scientific Instruments, 94, 085115. doi: 10.1063/5.0153042.
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).
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.
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.
V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner. Dataset, (2023)Related to: Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. doi: 10.1039/D2CP05793J.
M. Ruf, K. Taghizadeh Bajgirani, and H. Steeb. Dataset, (2023)Related to: Taghizadeh, K., Ruf, M., Luding, S., & Steeb, H. (2023). X-ray 3D imaging–based microunderstanding of granular mixtures: Stiffness enhancement by adding small fractions of soft particles. Proceedings of the National Academy of Sciences, 120(26), e2219999120. doi: 10.1073/pnas.2219999120.
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.
M. Rosenfelder, H. Ebel, and P. Eberhard. Dataset, (2023)Related to: Rosenfelder, M., Ebel, H., Eberhard, P. (2023). A Force-Based Formation Synthesis Approach for the Cooperative Transportation of Objects. In: Petrič, T., Ude, A., Žlajpah, L. (eds) Advances in Service and Industrial Robotics. RAAD 2023. Mechanisms and Machine Science, vol 135. Springer, Cham. doi: 10.1007/978-3-031-32606-6_37.
D. Fauser, and 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.
M. Ruf, and 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.
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. Ruf, D. Lee, and H. Steeb. Dataset, (2023)Related to: Ruf, M., Lee, D., & Steeb, H. (2023). A multifunctional mechanical testing stage for micro X-ray computed tomography. Review of Scientific Instruments, 94, 085115. doi: 10.1063/5.0153042.
M. Rosenfelder, H. Ebel, and P. Eberhard. Dataset, (2023)Related to: Rosenfelder, M., Ebel, H., Eberhard, P. (2023). Force-based organization and control scheme for the non-prehensile cooperative transportation of objects. Robotica, pp. 1-14, 2023. doi: 10.1017/S0263574723001704.
M. Ruf, and H. Steeb. Dataset, (2020)Related to: Lissa, S.; Ruf, M.; Steeb, H. & Quintal, B. (2020). Effects of crack roughness on attenuation caused by squirt flow in Carrara marble. SEG Technical Program Expanded Abstracts 2020, 2439-2443. doi: 10.1190/segam2020-3427789.1.
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).
J. Yang, S. Kondrat, C. Lian, H. Liu, A. Schlaich, and C. Holm. Dataset, (2023)Related to: J. Yang, S. Kondrat, C. Lian, H. Liu, A. Schlaich, und C. Holm, „Solvent Effects on Structure and Screening in Confined Electrolytes“, Phys. Rev. Lett., Bd. 131, Nr. 11, 118201, Sep. 2023. doi: 10.1103/PhysRevLett.131.118201.
F. Huber, P. Bürkner, D. Göddeke, and M. Schulte. Dataset, (2023)Related to: Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, MiriamKnowledge-Based Modeling of Simulation Behavior for Bayesian OptimizationComputational Mechanics (submitted).
J. Finkbeiner, S. Tovey, and C. Holm. Dataset, (2024)Related to: Jan Finkbeiner, Samuel Tovey, Christian Holm: Generating Minimal Training Sets for Machine Learned Potentials (2023). arXiv: 2309.03840.
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.
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.
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.
T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. Butz, and 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.
M. Ruf, J. Hommel, and H. Steeb. Dataset, (2022)Related to: Gehring, L., Weinhardt, F., Ruf, M., Hommel, J. & Steeb, H. (2022). Effects of enzymatically induced carbonate precipitation on capillary pressure-saturation relations. Minerals, 12(10), 1186. doi: 10.3390/min12101186.
M. Ruf, K. Taghizadeh Bajgirani, and H. Steeb. Dataset, (2021)Related to: Ruf, M., Taghizadeh, K. & Steeb, H. (2022). Multi-scale characterization of granular media by in situ laboratory X-ray computed tomography. GAMM Mitteilungen, 45(3-4), e202200011. doi: 10.1002/gamm.202200011.
M. Kelm, C. Bringedal, and 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.
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.
D. Fauser, and 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).
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.
M. Ruf, J. Hommel, and H. Steeb. Dataset, (2022)Related to: Gehring, L., Weinhardt, F., Ruf, M., Hommel, J. & Steeb, H. (2022). Effects of enzymatically induced carbonate precipitation on capillary pressure-saturation relations. Minerals, 12(10), 1186. doi: 10.3390/min12101186.
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.
M. Ruf, J. Hommel, and H. Steeb. Dataset, (2022)Related to: Gehring, L., Weinhardt, F., Ruf, M., Hommel, J. & Steeb, H. (2022). Effects of enzymatically induced carbonate precipitation on capillary pressure-saturation relations. Minerals, 12(10), 1186. doi: 10.3390/min12101186.
D. Fauser, M. Kuhn, and 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).
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.
X. Xu. Dataset, (2023)Related to: Xu, Xiang, Xi Zhang, Andrei Ruban, Siegfried Schmauder, and Blazej Grabowski. "Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al." Acta Materialia 255 (2023): 118986. doi: 10.1016/j.actamat.2023.118986.
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).