The development of usable visualization solutions is essential for ensuring both their adoption and effectiveness. User-centered design principles, which involve users throughout the entire development process, have been shown to be effective in numerous information visualization endeavors. We describe how we applied these principles in scientific visualization over a two year collaboration to develop a hybrid in situ/post hoc solution tailored towards combustion researcher needs. Furthermore, we examine the importance of user-centered design and lessons learned over the design process in an effort to aid others seeking to develop effective scientific visualization solutions.

Abstract We introduce a visual analysis system with GPU acceleration techniques for large sets of trajectories from complex dynamical systems. The approach is based on an interactive Boolean combination of subsets into a Focus+Context phase‐space visualization. We achieve high performance through efficient bitwise algorithms utilizing runtime generated GPU shaders and kernels. This enables a higher level of interactivity for visualizing the large multivariate trajectory data. We explain how our design meets a set of carefully considered analysis requirements, provide performance results, and demonstrate utility through case studies with many‐particle simulation data from two application areas.

Visualizing distributions from data samples as well as spatial and temporal trends of multiple variables is fundamental to analyzing the output of today's scientific simulations. However, traditional visualization techniques are often subject to a trade-off between visual clutter and loss of detail, especially in a large-scale setting. In this work, we extend the use of spatially organized histograms into a sophisticated visualization system that can more effectively study trends between multiple variables throughout a spatial domain. Furthermore, we exploit the use of isosurfaces to visualize time-varying trends found within histogram distributions. This technique is adapted into both an on-the-fly scheme as well as an in situ scheme to maintain real-time interactivity at a variety of data scales.

The use of large-scale scientific simulations that can represent physical systems using both particle and volume data simultaneously is gaining popularity as each of these reference frames has an inherent set of advantages when studying different phenomena. Furthermore, being able to study the dynamic evolution of these time varying data types is an integral part of nearly all scientific endeavors. However, the techniques available to scientists generally limit them to studying each reference frame separately making it difficult to draw connections between the two. In this work we present a novel method of feature exploration that can be used to investigate spatio-temporal patterns in both data types simultaneously. More specifically, we focus on how spatio-temporal subsets can be identified from both reference frames, and develop new ways of visually presenting the embedded information to a user in an intuitive manner. We demonstrate the effectiveness of our method using case studies of real world scientific datasets and illustrate the new types of exploration and analyses that can be achieved through this technique.

The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a "unit cell" based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer's needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved.

Cosmological simulations produce a multitude of data types whose large scale makes them difficult to thoroughly explore in an interactive setting. One aspect of particular interest to scientists is the evolution of groups of dark matter particles, or "halos," described by merger trees. However, in order to fully understand subtleties in the merger trees, other data types derived from the simulation must be incorporated as well. In this work, we develop a novel interactive linked-view visualization system that focuses on simultaneously exploring dark matter halos, their hierarchical evolution, corresponding particle data, and other quantitative information. We employ a parallel remote renderer and a local merger tree selection tool so that users can analyze large data sets interactively. This allows scientists to assess their simulation code, understand inconsistencies in extracted data, and intuitively understand simulation behavior on all scales. We demonstrate the effectiveness of our system through a set of case studies on large-scale cosmological data from the HACC (Hardware/Hybrid Accelerated Cosmology Code) simulation framework.

Studying the dynamic evolution of time-varying volumetric data is essential in countless scientific endeavors. The ability to isolate and track features of interest allows domain scientists to better manage large complex datasets both in terms of visual understanding and computational efficiency. This work presents a new trajectory-based feature tracking technique for use in joint particle/volume datasets. While traditional feature tracking approaches generally require a high temporal resolution, this method utilizes the indexed trajectories of corresponding Lagrangian particle data to efficiently track features over large jumps in time. Such a technique is especially useful for situations where the volume dataset is either temporally sparse or too large to efficiently track a feature through all intermediate timesteps. In addition, this paper presents a few other applications of this approach, such as the ability to efficiently track the internal properties of volumetric features using variables from the particle data. We demonstrate the effectiveness of this technique using real world combustion and atmospheric datasets and compare it to existing tracking methods to justify its advantages and accuracy.