Visualization, from simple line plots to complex high-dimensional visual analysis systems, has established itself throughout numerous domains to explore, analyze, and evaluate data. Applying such visualizations in the context of simulation science where High-Performance Computing (HPC) produces ever-growing amounts of data that is more complex, potentially multidimensional, and multimodal, takes up resources and a high level of technological experience often not available to domain experts. In this work, we present DaVE - a curated database of visualization examples, which aims to provide state-of-the-art and advanced visualization methods that arise in the context of HPC applications. Based on domain- or data-specific descriptors entered by the user, DaVE provides a list of appropriate visualization techniques, each accompanied by descriptions, examples, references, and resources. Sample code, adaptable container templates, and recipes for easy integration in HPC applications can be downloaded for easy access to high-fidelity visualizations. While the database is currently filled with a limited number of entries based on a broad evaluation of needs and challenges of current HPC users, DaVE is designed to be easily extended by experts from both the visualization and HPC communities.

Object selection in virtual environments is one of the most common and recurring interaction tasks. Therefore, the used technique can critically influence a system's overall efficiency and usability. IntenSelect is a scoring-based selection-by-volume technique that was shown to offer improved selection performance over conventional raycasting in virtual reality. This initial method, however, is most pronounced for small spherical objects that converge to a point-like appearance only, is challenging to parameterize, and has inherent limitations in terms of flexibility. We present an enhanced version of IntenSelect called IntenSelect+ designed to overcome multiple shortcomings of the original IntenSelect approach. In an empirical within-subjects user study with 42 participants, we compared IntenSelect+ to IntenSelect and conventional raycasting on various complex object configurations motivated by prior work. In addition to replicating the previously shown benefits of IntenSelect over raycasting, our results demonstrate significant advantages of IntenSelect+ over IntenSelect regarding selection performance, task load, and user experience. We, therefore, conclude that IntenSelect+ is a promising enhancement of the original approach that enables faster, more precise, and more comfortable object selection in immersive virtual environments.

Abstract Measured data often incorporates some amount of uncertainty, which is generally modeled as a distribution of possible samples. In this paper, we consider second‐order symmetric tensors with uncertainty. In the 3D case, this means the tensor data consists of 6 coefficients – uncertainty, however, is encoded by 21 coefficients assuming a multivariate Gaussian distribution as model. The high dimension makes the direct visualization of tensor data with uncertainty a difficult problem, which was until now unsolved. The contribution of this paper consists in the design of glyphs for uncertain second‐order symmetric tensors in 2D and 3D. The construction consists of a standard glyph for the mean tensor that is augmented by a scalar field that represents uncertainty. We show that this scalar field and therefore the displayed glyph encode the uncertainty comprehensively, i.e., there exists a bijective map between the glyph and the parameters of the distribution. Our approach can extend several classes of existing glyphs for symmetric tensors to additionally encode uncertainty and therefore provides a possible foundation for further uncertain tensor glyph design. For demonstration, we choose the well‐known superquadric glyphs, and we show that the uncertainty visualization satisfies all their design constraints.

Abstract The Parallel Vectors (PV) Operator extracts the locations of points where two vector fields are parallel. In general, these features are line structures. The PV operator has been used successfully for a variety of problems, which include finding vortex‐core lines or extremum lines. We present a new generic feature extraction method for multiple 3D vector fields: TheApproximate Parallel Vectors(APV) Operator extracts lines where all fields are approximately parallel. The definition of the APV operator is based on the application of PV for two vector fields that are derived from the given set of fields. The APV operator enables the direct visualization of features of vector field ensembles without processing fields individually and without causing visual clutter. We give a theoretical analysis of the APV operator and demonstrate its utility for a number of ensemble data.

Abstract The visual analysis of combustion processes is one of the challenges of modern flow visualization. In turbulent combustion research, the behaviour of the flame surface contains important information about the interactions between turbulence and chemistry. The extraction and tracking of this surface is crucial for understanding combustion processes. This is impossible to realize as a post‐process because of the size of the involved datasets, which are too large to be stored on disk. We present an on‐the‐fly method for tracking the flame surface directly during simulation and computing the local tangential surface deformation for arbitrary time intervals. In a massively parallel simulation, the data are distributed over many processes and only a single time step is in memory at any time. To satisfy the demands on parallelism and accuracy posed by this situation, we track the surface with independent micro‐patches and adapt their distribution as needed to maintain numerical stability. With our method, we enable combustion researchers to observe the detailed movement and deformation of the flame surface over extended periods of time and thus gain novel insights into the mechanisms of turbulence–chemistry interactions. We validate our method on analytic ground truth data and show its applicability on two real‐world simulations.

Glyphs are a powerful tool for visualizing second-order tensors in a variety of scientic data as they allow to encode physical behavior in geometric properties. Most existing techniques focus on symmetric tensors and exclude non-symmetric tensors where the eigenvectors can be non-orthogonal or complex. We present a new construction of 2d and 3d tensor glyphs based on piecewise rational curves and surfaces with the following properties: invariance to (a) isometries and (b) scaling, (c) direct encoding of all real eigenvalues and eigenvectors, (d) one-to-one relation between the tensors and glyphs, (e) glyph continuity under changing the tensor. We apply the glyphs to visualize the Jacobian matrix fields of a number of 2d and 3d vector fields.