L. Werneck, E. Yildiz, M. Han, M. Keip, M. Sitti, und 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. 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.
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.
P. Reiser, J. Aguilar, A. Guthke, und P. Bürkner. Software, (2024)Related to: Reiser P., Aguilar J. E., Guthke A., & Bürkner P. C. (2023). Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. ArXiv preprint 2312.05153. arXiv: 2312.05153.
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.
