- Arnold, C., Sharma, S., Weigel, T., & Greenberg, D. S. (2024): Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5). Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024
- Nalepa, J., Tulczyjew, Ł., Le Saux, B., Longépé, N., Ruszczak, B., Wijata, A. M., Smykała, K., Myller, M., Kawulok, M., Kuzu, R. S., Albrecht, F., Arnold, C., Alasawedah, M., Angeli, S., Nobileau, D., Ballabeni, A., Lotti, A., Locarini, A., Modenini, D., Tortora, P., & Gumiela, M. (2024): Estimating Soil Parameters From Hyperspectral Images: A benchmark dataset and the outcome of the HYPERVIEW challenge. IEEE Geoscience and Remote Sensing Magazine, 12(3), 35 63. https://doi.org/10.1109/MGRS.2024.3394040
- Karimpouli, S.; Caus, D.; Grover, H.; Martínez-Garzón, P.; Bohnhoff, M.; Beroza, G.C.; Dresen, G.; Goebel, T.; Weigel, T.; Kwiatek, G. (2023): Explainable machine learning for labquake prediction using catalog-driven features, Earth and Planetary Science Letters, Volume 622, 118383, https://doi.org/10.1016/j.epsl.2023.118383
- Karimpouli, S.; Caus, D.; Grover, H.; Martínez-Garzón, P.; Bohnhoff, M.; Beroza, G. C.; Dresen, G.; Goebel, T.; Weigel, T.; Kwiatek, G. (2023): Identification of wave breaking from nearshore wave-by-wave records. Physics of Fluids 35, 092105. https://doi.org/10.1063/5.0165053
- Piraud, M.; Camero, A.; Götz, M.; Kesselheim, S.; Steinbach, P.; Weigel, T. (2023): Providing AI expertise as an infrastructure in academia, Patterns, Volume 4, Issue 8, 2023, 100819, https://doi.org/10.1016/j.patter.2023.100819