M. Gültig, J. Range, B. Schmitz, und 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, 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.
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, und 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, und 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, 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. Gültig, J. Range, B. Schmitz, und 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, und 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, und 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, und 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, und 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, und 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, und 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.
M. Schmitt. Software, (2023)Related to: Schmitt, M., Radev, S. T., Bürkner, P.-C. (2023). Meta-Uncertainty in Bayesian Model Comparison. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:11-29, 2023.
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.
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.
M. Gültig, J. Range, B. Schmitz, und 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, 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.
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, 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.
I. Wochner, und 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, 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.
M. Gültig, J. Range, B. Schmitz, und 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, und 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, und 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, und 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, 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).
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.
J. Magiera. Dataset, (2024)Related to: Jim Magiera, Deep Ray, Jan S. Hesthaven, Christian Rohde, Constraint-aware neural networks for Riemann problems, Journal of Computational Physics, Volume 409, 2020, 109345. doi: 10.1016/j.jcp.2020.109345.
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, und 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, und 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, und 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.
A. Schlaich. Dataset, (2023)Related to: The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants. Bolik Stéphanie, Schlaich Alexander, Mukhina Tetiana, Amato Alberto, Bastien Olivier, Schneck Emanuel, Demé Bruno, Jouhet Juliette. bioRxiv 2023.01.24.525350. doi: 10.1101/2023.01.24.525350.
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.