We present an interactive approach to analyse flow fields using a new type of particle system, which is composed of autonomous particles exploring the flow. While particles provide a very intuitive way to visualize flows, it is a challenge to capture the important features with such systems. Particles tend to cluster in regions of low velocity and regions of interest are often sparsely populated. To overcome these disadvantages, we propose an automatic adaption of the particle density with respect to local importance measures. These measures are user defined and the systems sensitivity to them can be adjusted interactively. Together with the particle history, these measures define a probability for particles to multiply or die, respectively. There is no communication between the particles and no neighbourhood information has to be maintained. Thus, the particles can be handled in parallel and support a real‐time investigation of flow fields. To enhance the visualization, the particles' properties and selected field measures are also used to specify the systems rendering parameters, such as colour and size. We demonstrate the effectiveness of our approach on different simulated vector fields from technical and medical applications.

In this work we explore evolutionary algorithms for selected a visualization application. We demonstrate its potential using an example from flow visualization showing promising first results. Evolutionary algorithms, as guided search approach, find close-to-optimal solutions with respect to some fitness function in an iterative process using biologically motivated mechanisms like selection, mutation and recombination. As such, they provide a powerful alternative to filtering methods commonly used in visualization where the space of possible candidates is densely sampled in a pre-processing step from which the best candidates are selected and visualized. This approach however tends to be increasingly inefficient with growing data size or expensive candidate computations resulting in large pre-processing times. We present an evolutionary algorithm for the problem of streamline selection to highlight features of interest in flow data. Our approach directly optimizes the solution candidates with respect to a user selected fitness function requiring significantly less computations. At the same time the problem of possible under-sampling is solved since we are not tied to a preset resolution. We demonstrate our approach on the well-known flow around an obstacle as case with a two-dimensional search space. The blood flow in an aneurysm serves as an example with a three-dimensional search space. For both, the achieved results are comparable to line filtering approaches with much less line computations.

This paper presents an automatic selection of viewpoints, forming a camera path, to support the exploration of cerebral aneurysms. Aneurysms bear the risk of rupture with fatal consequences for the patient. For the rupture risk evaluation, a combined investigation of morphological and hemodynamic data is necessary. However, the extensive nature of the time-dependent data complicates the analysis. During exploration, domain experts have to manually determine appropriate views, which can be a tedious and time-consuming process. Our method determines optimal viewpoints automatically based on input data such as wall thickness or pressure. The viewpoint selection is modeled as an optimization problem. Our technique is applied to five data sets and we evaluate the results with two domain experts by conducting informal interviews.

The CrossDrive Project develops Distributed and Collaborative Infrastructure based on advanced Immersive Virtual Reality tools for the analysis and management of Scientific Data and Operational Activities of planetary spacecraft. The Collaborative Workspace encompasses advanced technological solutions for central storage processing, 3D visualisation and Virtual Presence in Immersive Virtual Reality environments, to support Space Data Analysis and Space Operations. Science objectives are share and correlate Atmospheric data, analysis and simulations based on the actual main Mars’ satellites (MEX and MRO); compare and correlate data for Geology and Geodesy; benchmark satellite and ground based Mars atmospheric measurements.

Atmospheric phenomena of Mars can be highly dynamic and have daily and seasonal variations. Planetary-scale wavelike disturbances, for example, are frequently observed in Mars' polar winter atmosphere. Possible sources of the wave activity were suggested to be dynamical instabilities and quasi-stationary planetary waves, i.e. waves that arise predominantly via zonally asymmetric surface properties. For a comprehensive understanding of these phenomena, single layers of altitude have to be analyzed carefully and relations between different atmospheric quantities and interaction with the surface of Mars have to be considered. The CROSS DRIVE project tries to address the presentation of those data with a global view by means of virtual reality techniques. Complex orbiter data from spectrometer and observation data from Earth are combined with global circulation models and high-resolution terrain data and images available from Mars Express or MRO instruments. Scientists can interactively extract features from those dataset and can change visualization parameters in real-time in order to emphasize findings. Stereoscopic views allow for perception of the actual 3D behavior of Mars's atmosphere. A very important feature of the visualization system is the possibility to connect distributed workspaces together. This enables discussions between distributed working groups. The workspace can scale from virtual reality systems to expert desktop applications to web-based project portals. If multiple virtual environments are connected, the 3D position of each individual user is captured and used to depict the scientist as an avatar in the virtual world. The appearance of the avatar can also scale from simple annotations to complex avatars using tele-presence technology to reconstruct the users in 3D. Any change of the feature set (annotations, cutplanes, volume rendering, etc.) within the VR is immediately exchanged between all connected users. This allows that everybody is always aware of what is visible and discussed. The discussion is supported by audio and interaction is controlled by a moderator managing turn-taking presentations. A use case execution proved a success and showed the potential of this immersive approach.

The three year European research project CROSS DRIVE (Collaborative Rover Operations and Planetary Science Analysis System based on Distributed Remote and Interactive Virtual Environments) started in January 2014. The research and development within this project is motivated by three use case studies: landing site characterization, atmospheric science and rover target selection.

Currently the implementation for the second use case is in its final phase. Here, the requirements were generated based on the domain experts input and lead to development and integration of appropriate methods for visualization and analysis of atmospheric data. The methods range from volume rendering, interactive slicing, iso-surface techniques to interactive probing. All visualization methods are integrated in DLR’s Terrain Rendering application. With this, the high resolution surface data visualization can be enriched with additional methods appropriate for atmospheric data sets. This results in an integrated virtual environment where the scientist has the possibility to interactively explore his data sets directly within the correct context. The data sets include volumetric data of the martian atmosphere, precomputed two dimensional maps and vertical profiles. In most cases the surface data as well as the atmospheric data has global coverage and is of time dependent nature. Furthermore, all interaction is synchronized between different connected application  instances, allowing for collaborative sessions between distant experts.

Space exploration missions have produced large data of immense value, to both research and the planning and operating of future missions. However, current datasets and simulation tools fragment teamwork, especially across disciplines and geographical location. The aerospace community already exploits virtual reality for purposes including space tele-robotics, interactive 3D visualization, simulation and training. However, collaborative virtual environments are yet to be widely deployed or routinely used in space projects. Advanced immersive and collaborative visualization systems have the potential for enhancing the efficiency and efficacy of data analysis, simplifying visual benchmarking, presentations and discussions. We present preliminary results of the EU funded international project CROSS DRIVE, which develops an infrastructure for collaborative workspaces for space science and missions. The aim is to allow remote scientific and engineering experts to collectively analyze and interpret combined datasets using shared simulation tools. The approach is to combine advanced 3D visualization techniques and interactive tools in conjunction with immersive virtuality telepresence. This will give scientists and engineers the impression of teleportation from their respective buildings across Europe, to stand together on a planetary surface, surrounded by the information and tools that they need. The conceptual architecture and proposed realization of the collaborative workspace are described. ESA's planned ExoMars mission provides the use-case for deriving user requirements and evaluating our implementation.

The European research project CROSS DRIVE (Collaborative Rover Operations and Planetary Science Analysis System based on Distributed Remote and Interactive Virtual Environments) aims at developing an innovative collaborative workspace infrastructure enabling re- mote scientific and engineering experts to collectively analyze and interpret combined datasets using shared simulation tools. The three year project started in January 2014 and unites best European expertise in the fields of planetary research and Mars science, Virtual Reality (VR), atmospheric science and research as well as rover mission planning.

Many scientific applications deal with data from a multitude of different sources, e.g., measurements, imaging and simulations. Each source provides an additional perspective on the phenomenon of interest, but also comes with specific limitations, e.g. regarding accuracy, spatial and temporal availability. Effectively combining and analyzing such multimodal and partially incomplete data of limited accuracy in an integrated way is challenging. In this work, we outline an approach for an integrated analysis and visualization of the atmospheric impact of volcano eruptions. The data sets comprise observation and imaging data from satellites as well as results from numerical particle simulations. To analyze the clouds from the volcano eruption in the spatiotemporal domain we apply topological methods. We show that topology-related extremal structures of the data support clustering and comparison. We further discuss the robustness of those methods with respect to different properties of the data and different parameter setups. Finally we outline open challenges for the effective integrated visualization using topological methods.

The analysis of data that captures volcanic eruptions and their atmospheric aftermath plays an important role for domain experts to gain a deeper understanding of the volcanic eruption and their consequences for atmosphere, climate and air traffic. Thereby, one major challenge is to extract and combine the essential information, which is spread over various, mostly sparse data sources. This requires a careful integration of each data set with its strength and limitations. The sparse, but more reliable measurement data is mainly used to calibrate the more dense simulation data. This work combines a collection of visualization approaches into an exploitative framework. The goal is to support the domain experts to build a complete picture of the situation. But it is also important to understand the individual data sources, the wealth of their information and the quality of the simulation results. All presented methods are designed for direct interaction with the data from different perspectives rather than the sole generation of some final images.