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.
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.
F. Kaiser. Dataset, (2021)Related to: Nanofabricated and integrated colour centres in silicon carbide with high-coherence spin-optical properties, Charles Babin and Rainer Stöhr and Naoya Morioka and Tobias Linkewitz and Timo Steidl and Raphael Wörnle and Di Liu and Erik Hesselmeier and Vadim Vorobyov and Andrej Denisenko and Mario Hentschel and Christian Gobert and Patrick Berwian and Georgy V. Astakhov and Wolfgang Knolle and Sridhar Majety and Pranta Saha and Marina Radulaski and Nguyen Tien Son and Jawad Ul-Hassan and Florian Kaiser and Jörg Wrachtrup, (2021). arXiv: 2109.04737.
F. Sardi. Dataset, (2021)Related to: F. Sardi, T. Kornher, M. Widmann, et al.. Scalable production of solid-immersion lenses for quantum emitters in silicon carbide. Appl. Phys. Lett. 117, 022105 (2020). doi: 10.1063/5.0011366.
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. 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.
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.
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.
D. Holzmüller, and I. Steinwart. Software, (2022)Related to: David Holzmüller and Ingo Steinwart. Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, 2020. arXiv: 2002.04861.
C. Lohrmann, and C. Holm. Software, (2023)Related to: Lohrmann, C. and Holm, C.: A novel model for biofilm initiation in porous media flow (under revision).
S. Tovey, S. Krippendorf, K. Nikolaou, and C. Holm. Software, (2023)Related to: Tovey, S. J. et al. (2023) ‘Towards a phenomenological understanding of neural networks: data’, Machine Learning: Science and Technology. doi: 10.1088/2632-2153/acf099.
C. Bühler, T. Ilg, and H. Büchler. Dataset, (2023)Related to: C. Bühler, T. Ilg, and H. P. Büchler, Quantum fluctuations in one-dimensional supersolids, Physical Review Research 5, 033092 (2023). doi: 10.1103/PhysRevResearch.5.033092.