4 citations · 11 across the 10 of their papers we have counts for
12 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…
GrINd: Grid Interpolation Network for Scattered Observations
Andrzej Dulny, Paul Heinisch, Andreas Hotho +1
Predicting the evolution of spatiotemporal physical systems from sparse and scattered observational data poses a significant challenge in various scientific domains. Traditional me…
Global Vegetation Modeling with Pre-Trained Weather Transformers
Pascal Janetzky, Florian Gallusser, Simon Hentschel +2
Accurate vegetation models can produce further insights into the complex interaction between vegetation activity and ecosystem processes. Previous research has established that lon…
TaylorPDENet: Learning PDEs from non-grid Data
Paul Heinisch, Andrzej Dulny, Anna Krause +1
Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models. Previous approaches assume the measurements to…
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data
Andrzej Dulny, Andreas Hotho, Anna Krause
Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data)…
Semi-unsupervised Learning for Time Series Classification
Padraig Davidson, Michael Steininger, André Huhn +2
Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is…
Open ERP System Data For Occupational Fraud Detection
Julian Tritscher, Fabian Gwinner, Daniel Schlör +2
Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems…
NeuralPDE: Modelling Dynamical Systems from Data
Andrzej Dulny, Andreas Hotho, Anna Krause
Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs). Modelling such dynamical systems using Neural Networks i…
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…