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, V. Zaverkin, J. Kästner, and 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.
S. Reuschen, T. Xu, and W. Nowak. Dataset, (2020)Related to: Reuschen, S., Xu, T., Nowak, W., 2020. Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC. Advances in Water Resources 141, 103614. doi: 10.1016/j.advwatres.2020.103614.
L. Scholz, and C. Bringedal. Dataset, (2021)Related to: Scholz, L., Bringedal, C. A Three-Dimensional Homogenization Approach for Effective Heat Transport in Thin Porous Media. Transp Porous Med (2022). doi: 10.1007/s11242-022-01746-y.
S. Schulz, C. Bringedal, and S. Ackermann. Dataset, (2021)Related to: SimTech Project work "Herleitung reduzierter Modelle einer Zweiphasenströmung zwischen parallelen Platten mit Slip-Bedingungen".
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.