| A comparative study of convolutional neural network models for wind field downscaling K Höhlein, M Kern, T Hewson, R Westermann Meteorological Applications 27 (6), e1961, 2020 | 105 | 2020 |
| An emergent space for distributed data with hidden internal order through manifold learning FP Kemeth, SW Haugland, F Dietrich, T Bertalan, K Höhlein, Q Li, ... IEEE Access 6, 77402-77413, 2018 | 28 | 2018 |
| Postprocessing of ensemble weather forecasts using permutation-invariant neural networks K Höhlein, B Schulz, R Westermann, S Lerch Artificial Intelligence for the Earth Systems 3 (1), e230070, 2024 | 16 | 2024 |
| Learning multiple‐scattering solutions for sphere‐tracing of volumetric subsurface effects L Leonard, K Hoehlein, R Westermann Computer graphics forum 40 (2), 165-178, 2021 | 12 | 2021 |
| Lyapunov spectra and collective modes of chimera states in globally coupled Stuart-Landau oscillators K Höhlein, FP Kemeth, K Krischer Physical Review E 100 (2), 022217, 2019 | 10 | 2019 |
| Neural fields for interactive visualization of statistical dependencies in 3D simulation ensembles F Farokhmanesh, K Höhlein, C Neuhauser, T Necker, M Weissmann, ... arXiv preprint arXiv:2307.02203, 2023 | 5 | 2023 |
| Evaluation of Volume Representation Networks for Meteorological Ensemble Compression. K Höhlein, S Weiss, T Necker, M Weissmann, T Miyoshi, R Westermann VMV, 9-16, 2022 | 4 | 2022 |
| GPU accelerated scalable parallel coordinates plots J Stumpfegger, K Höhlein, G Craig, R Westermann Computers & Graphics 109, 111-120, 2022 | 3 | 2022 |
| Deep learning–based parameter transfer in meteorological data F Farokhmanesh, K Höhlein, T Necker, M Weissmann, T Miyoshi, ... Artificial Intelligence for the Earth Systems 2 (1), e220024, 2023 | 2 | 2023 |
| Towards Operational Downscaling of Low Resolution Wind Fields Using Neural Networks M Kern, K Höhlein, T Hewson, R Westermann EGU General Assembly Conference Abstracts, 5447, 2020 | 2 | 2020 |
| Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods J Chen, K Höhlein, S Lerch arXiv preprint arXiv:2502.04409, 2025 | | 2025 |
| Data-Driven Modeling and Analysis of Numerical Weather Predictions K Höhlein Technische Universität München, 2025 | | 2025 |
| Topographic Visualization of Near-surface Temperatures for Improved Lapse Rate Estimation K Höhlein, T Hewson, R Westermann arXiv preprint arXiv:2406.11894, 2024 | | 2024 |
| 2m Temperature Downscaling with Dynamic Lapse Rates K Höhlein, T Hewson EMS2023, 2023 | | 2023 |
| Spatial representation learning for ensemble weather simulations using invariant variational autoencoders J Chen, K Höhlein, S Lerch EGU General Assembly Conference Abstracts, EGU-1825, 2023 | | 2023 |
| Probabilistic Downscaling of Climate Variables Using Denoising Diffusion Probabilistic Models D Papikyan, K Höhlein, R Westermann | | 2022 |
Westermann RüdigerProfessor für Informatik, Technische Universität MünchenPotvrđena adresa e-pošte na tum.de
