5,033 research outputs found
Reducing Occlusion in Cinema Databases through Feature-Centric Visualizations
In modern supercomputer architectures, the I/O capabilities do not keep up with the computational speed. Image-based techniques are one very promising approach to a scalable output format for visual analysis, in which a reduced output that corresponds to the visible state of the simulation is rendered in-situ and stored to disk. These techniques can support interactive exploration of the data through image compositing and other methods, but automatic methods of highlighting data and reducing clutter can make these methods more effective. In this paper, we suggest a method of assisted exploration through
the combination of feature-centric analysis with image space techniques and show how the reduction of the data to features of interest reduces occlusion in the output for a set of example applications
Interactive 3D visualization for theoretical Virtual Observatories
Virtual Observatories (VOs) are online hubs of scientific knowledge. They
encompass a collection of platforms dedicated to the storage and dissemination
of astronomical data, from simple data archives to e-research platforms
offering advanced tools for data exploration and analysis. Whilst the more
mature platforms within VOs primarily serve the observational community, there
are also services fulfilling a similar role for theoretical data. Scientific
visualization can be an effective tool for analysis and exploration of datasets
made accessible through web platforms for theoretical data, which often contain
spatial dimensions and properties inherently suitable for visualization via
e.g. mock imaging in 2d or volume rendering in 3d. We analyze the current state
of 3d visualization for big theoretical astronomical datasets through
scientific web portals and virtual observatory services. We discuss some of the
challenges for interactive 3d visualization and how it can augment the workflow
of users in a virtual observatory context. Finally we showcase a lightweight
client-server visualization tool for particle-based datasets allowing
quantitative visualization via data filtering, highlighting two example use
cases within the Theoretical Astrophysical Observatory.Comment: 10 Pages, 13 Figures, Accepted for Publication in Monthly Notices of
the Royal Astronomical Societ
Visitation Graphs: Interactive Ensemble Visualization with Visitation Maps
Modern applications in computational science are increasingly focusing on understanding uncertainty in models and parameters in simulations. In this paper, we describe visitation graphs, a novel approximation technique for the well-established visualization of steady 2D vector field ensembles using visitation maps. Our method allows the efficient and robust computation of arbitrary visitation maps for vector field ensembles. A pre-processing step that can be parallelized to a high degree eschews the needs to store every ensemble member and to re-calculate every time the start position of the visitation map is changed. Tradeoffs between accuracy of generated visitation maps on one side and pre-processing time and storage requirements on the other side can be made. Instead of downsampling ensemble members to a storable size, coarse visitation graphs can be stored, giving more accurate visitation maps while still reducing the amount of data. Thus accurate visitation map creation is possible for ensembles where the traditional visitation map creation is prohibitive. We describe our approach in detail and demonstrate its effectiveness and utility on examples from Computational Fluid Dynamics
Statistical Parameter Selection for Clustering Persistence Diagrams
International audienceIn urgent decision making applications, ensemble simulations are an important way to determine different outcome scenarios based on currently available data. In this paper, we will analyze the output of ensemble simulations by considering so-called persistence diagrams, which are reduced representations of the original data, motivated by the extraction of topological features. Based on a recently published progressive algorithm for the clustering of persistence diagrams, we determine the optimal number of clusters, and therefore the number of significantly different outcome scenarios, by the minimization of established statistical score functions. Furthermore, we present a proof-of-concept prototype implementation of the statistical selection of the number of clusters and provide the results of an experimental study, where this implementation has been applied to real-world ensemble data sets
- …