Visualization and visual analysis play important roles in exploring, analyzing, and
presenting scientific data. In many disciplines, data and model scenarios are becoming …

Colormaps are a vital method for users to gain insights into data in a visualization. With a
good choice of colormaps, users are able to acquire information in the data more effectively …

Over the last decade, ensemble visualization has witnessed a significant development due
to the wide availability of ensemble data, and the increasing visualization needs from a …

Various case studies in different application domains have shown the great potential of
visual parameter space analysis to support validating and using simulation models. In order …

We propose InSituNet, a deep learning based surrogate model to support parameter space
exploration for ensemble simulations that are visualized in situ. In situ visualization …

With data comes uncertainty, which is a widespread and frequent phenomenon in data
science and analysis. The amount of information available to us is growing exponentially …

A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions
by weighted summary scores. Weights, however, are often abstract to the decision maker …

In this paper we address the difficult problem of parameter-finding in image segmentation.
We replace a tedious manual process that is often based on guess-work and luck by a …

We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the
parameter space of ocean climate simulations. Parameter space exploration is important for …

In meteorology, cluster analysis is frequently used to determine representative trends in
ensemble weather predictions in a selected spatio-temporal region, eg, to reduce a set of …