5 papers
Exploring Design Choices for Autoregressive Deep Learning Climate Models
Florian Gallusser, Simon Hentschel, Anna Krause +1
Deep Learning models have achieved state-of-the-art performance in medium-range weather prediction but often fail to maintain physically consistent rollouts beyond 14 days. In cont…
Physical knowledge improves prediction of EM Fields
Andrzej Dulny, Farzad Jabbarigargari, Andreas Hotho +3
We propose a 3D U-Net model to predict the spatial distribution of electromagnetic fields inside a radio-frequency (RF) coil with a subject present, using the phase, amplitude, and…
Anomaly Detection in Beehives: An Algorithm Comparison
Padraig Davidson, Michael Steininger, Florian Lautenschlager +2
Sensor-equipped beehives allow monitoring the living conditions of bees. Machine learning models can use the data of such hives to learn behavioral patterns and find anomalous even…
Deep Learning for Climate Model Output Statistics
Michael Steininger, Daniel Abel, Katrin Ziegler +3
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitatio…
Anomaly Detection in Beehives using Deep Recurrent Autoencoders
Padraig Davidson, Michael Steininger, Florian Lautenschlager +3
Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to le…