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.
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, 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, 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. 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. arXiv: 2210.07182.
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. 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.
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.
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.
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.
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, 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. 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.
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.
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.
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.
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.