Weather forecasting, focusing on global and regional temperature and geopotential prediction
Determining links between modeling cognitive and physical processes
Sustainability and climate protection
Biographical information
Since March 2019: Doctoral student in the Cognitive Modeling group, Eberhard Karls University of Tuebingen and scholar of the International Max Planck Research School for Intelligent Systems (IMPRS-IS) program
2015 - 2018: Master of Cogntitive Science, Eberhard Karls University of Tübingen
2012 - 2015: Bachelor of Cognitive Science, Eberhard Karls University of Tübingen
Supervision
2022
BSc Emil Breustedt (2022), Contrasting Traditional and Learned Super-Resolution Methods on Weather Maps over Germany.
Lab project Cosku-Can Horuz (2022), Inferring Unknown Boundary Conditions of Spatiotemporal Advection-Diffusion Processes via Finite Volume Neural Network. Co-supervised with Sebastian Otte.
BSc Andreas Schaible (2022), Exploration eines Physik-Motivierten GNNs auf Raumzeitlich Heterogenen Observationsdaten.
MSc Adrian Stock (2022), Modeling Weather Station Data with Graph Neural Networks. Co-supervised with Tobias Menge and Jannik Thümmel.
BSc Adrian Welter (2022), Applying DISTANA to Sequential Three-Dimensional Fluid Flow Data.
2021
BSc Magnus Kaut (2021), Implementing DISTANA in PyTorch Geometric and Deep Graph Library.
BSc Linda Ulmer (2021), Time-Constrained Active Tuning in Recurrent Neural Networks. Co-supervised with Sebastian Otte.
BSc Arthur Otte (2021), Analysis and Extension of a Neural Network Model for Spatio-Temporal Prediction.
MSc Lars Gehrke (2021), GPU Implementation of the Graph Neural Network DISTANA with CUDA.
2020
BSc Lina Gönnheimer (2020), Predicting a 2D Wave Pattern with an Echo State Network.
MSc Tobias Menge (2020), Implementation and Evaluation of REPRISE for DISTANA regarding Static Context Inference for Wave Prediction.
2019
MSc Andreas Schmitt (2019), Exploring the Capabilities of a Spatio-Temporal, RNN-based Architecture using Simulated Fluid Data and Considering Contextual Information.
Lab project Leon Varga (2019), Introduce Sparse Information Processing in a Dense Grid by using REPRISE.
Teaching
2022
Lecture: Generative and Recurrent Neural Networks. Tutoring and holding lecture about Graph Neural Networks.
Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, and Martin V. Butz (2022). Composing Partial Differential Equations with Physics-Aware Neural Networks. Submitted to ICML.
2021
Matthias Karlbauer, Tobias Menge, Sebastian Otte, Hendrik P. A. Lensch, Thomas Scholten, Volker Wulfmeyer, and Martin V. Butz (2021). Latent State Inference in a Proceedings of the International Conference on Artificial Neural Networks (ICANN), 384-395.
Melvin Ciurletti, Manuel Traub, Matthias Karlbauer, Martin V. Butz, and Sebastian Otte (2021). Signal Denoising with Recurrent Spiking Neural Networks and Active Tuning. Proceedings of the International Conference on Artificial Neural Networks (ICANN), 220-232.
Timothy Praditia, Matthias Karlbauer, Sebastian Otte, Sergey Oladyshkin, Martin V. Butz, and Wolfgang Nowak (2021). Finite Volume Neural Network: Modeling Subsurface Contaminant Transport. ICLR workshop on Deep Learning for Simulation (SimDL).
Matthias Karlbauer, Sebastian Otte, Hendrik Lensch, Thomas Scholten, Volker Wulfmeyer, and Martin Butz (2020). Inferring, Predicting, and Denoising Causal Wave Dynamics. Proceedings of the International Conference on Artificial Neural Networks (ICANN), 566-577.
Matthias Karlbauer, Sebastian Otte, Hendrik Lensch, Thomas Scholten, Volker Wulfmeyer, and Martin Butz (2020). A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN).
2018
Ruben Ellinghaus, Matthias Karlbauer, Karin M. Bausenhart, and Rolf Ulrich (2018). On the time-course of automatic response activation in the Simon task. Psychological research, 1-10.
