M.Sc. Kevin Höhlein

I am a PhD student at the chair of Computer Graphics and Visualization (TUM), researching applications of data science and machine learning techniques in the context meteorological data analysis. My research interests include the following topics:   

• Deep learning for weather prediction and meteorological data analysis
• Explainability of machine learning predictions
• Probabilistic modeling and generative deep learning
• Nonlinear dynamics and chaos theory

Topographic Visualization of Near-surface Temperatures for Improved Lapse Rate Estimation
Kevin Höhlein, Timothy Hewson, Rüdiger Westermann
arXiv preprint, arXiv:2406.11894 (2024).

Postprocessing of Ensemble Weather Forecasts Using Permutation-Invariant Neural Networks
Kevin Höhlein, Benedikt Schulz, Rüdiger Westermann, Sebastian Lerch
Artif. Intell. Earth Syst., 3, e230070 (2024).

Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles
Fatemeh Farokhmanesh, Kevin Höhlein, Christoph Neuhauser, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann
In M. Guthe,  & T. Grosch (Eds.), Vision, Modeling, and Visualization. The Eurographics Association (2023).

Deep Learning-based Parameter Transfer in Meteorological Data
Fatemeh Farokhmanesh, Kevin Höhlein, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann
Artif. Intell. Earth Syst., 2, e220024 (2023).

GPU Accelerated Scalable Parallel Coordinates Plots
Josef Stumpfegger, Kevin Höhlein, George Craig, Rüdiger Westermann
Computers & Graphics, 109, pp. 111-120 (2022).

Evaluation of Volume Representation Networks for Meteorological Ensemble Compression
Kevin Höhlein, Sebastian Weiss, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann
In J. Bender, M. Botsch, & D. A. Keim (Eds.), Vision, Modeling, and Visualization. The Eurographics Association (2022).

Learning Multiple-Scattering Solutions for Sphere-Tracing of Volumetric Subsurface Effects
Ludwig Leonard, Kevin Höhlein, Rüdiger Westermann
Computer Graphics Forum, 40(2), pp. 165-178 (2021).

A Comparative Study of Convolutional Neural Network Models for Wind Field Downscaling
Kevin Höhlein, Michael Kern, Timothy Hewson, Rüdiger Westermann
Meteorological Applications, 27, e1961 (2020).

Lyapunov Spectra and Collective Modes of Chimera States in Globally Coupled Stuart-Landau Oscillators
Kevin Höhlein, Felix P. Kemeth, Katharina Krischer
Phys. Rev. E, 100, 022217 (2019).

An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning
Felix P. Kemeth, Sindre W. Haugland, Felix Dietrich, Tom Bertalan, Kevin Höhlein, Qianxiao Li, Erik M. Bollt, Ronen Talmon, Katharina Krischer, Ioannis G. Kevrekidis
IEEE Access, 6, pp. 77402-77413 (2018).