@conference {693,
	title = {Measuring Event Probabilities in Uncertain Scalar Datasets using Gaussian Processes},
	booktitle = {WSCG},
	year = {2016},
	abstract = {In this paper, we show how the concept of Gaussian process regression can be used to determine potential events in
scalar data sets. As a showcase, we will investigate climate data sets in order to identify potential extrem weather
events by deriving the probabilities of their appearances. The method is implemented directly on the GPU to ensure
interactive frame rates and pixel precise visualizations. We will see, that this approach is especially well suited for
sparse sampled data because of its reconstruction properties.},
	author = {Steven Schlegel and Sebastian Volke and Gerik Scheuermann}
}
@conference {654,
	title = {Interactive GPU-based Visualization of Scalar Data with Gaussian Distributed Uncertainty},
	booktitle = {Vision, Modeling, and Visualization},
	year = {2015},
	address = {Aachen},
	abstract = {We present a GPU-based approach to visualize samples of normally distributed uncertain, three-dimensional
scalar data. Our approach uses a mathematically sound interpolation scheme, i.e., Gaussian process regression.
The focus of this work is to demonstrate, that GP-regression can be used for interpolation in practice, despite
the high computational costs. The potential of our method is demonstrated by an interactive volume rendering of
three-dimensional data, where the gradient estimation is directly computed by the field function without the need
of additional sample points of the underlying data. We illustrate our method using three-dimensional data sets of
the medical research domain.},
	author = {Steven Schlegel and Mathias Goldau and Gerik Scheuermann}
}
@conference {528,
	title = {Determining and Visualizing Potential Sources of Floods},
	booktitle = {EuroVis Workshop on Visualisation in Environmental Sciences},
	year = {2013},
	address = {Leipzig},
	abstract = {In this paper, we visually analyze spatio-temporal patterns of different hydrologic parameters relevant for flooding.
On the basis of data from climate simulations with a high resolution regional atmosphere model, several extreme
events are selected for different river catchments in Germany. By visually comparing the spatial distribution of the
main contributions to the run-off along with their temporal evolution for a time period in the 20th and the 21th
century, impacts of climate change on the hydrological cycle can be identified.
},
	author = {Steven Schlegel and B{\"o}ttinger, M. and Mario Hlawitschka and Gerik Scheuermann}
}
@article {431,
	title = {On the Interpolation of Data with Normally Distributed Uncertainty for Visualization},
	journal = {IEEE Transactions on Visualization and Computer Graphics (Proceedings Scientific Visualization / Information Visualization 2012)},
	volume = {18},
	year = {2012},
	month = {2012},
	pages = {10},
	chapter = {2305},
	abstract = {In many fields of science or engineering, we are confronted with uncertain data. For that reason, the visualization of uncertainty received a lot of attention, especially in recent years. In the majority of cases, Gaussian distributions are used to describe uncertain behavior, because they are able to model many phenomena encountered in science. Therefore, in most applications uncertain data is (or is assumed to be) Gaussian distributed. If such uncertain data is given on fixed positions, the question of interpolation arises for many visualization approaches. In this paper, we analyze the effects of the usual linear interpolation schemes for visualization of Gaussian distributed data. In addition, we demonstrate that methods known in geostatistics and machine learning have favorable properties for visualization purposes in this case.},
	url = {http://www.informatik.uni-leipzig.de/~schlegel/Paper/2012/Visweek2012/paper.pdf},
	author = {Steven Schlegel and Nico Korn and Gerik Scheuermann}
}
