Visual Trends Analysis in Time-Varying Ensembles.

Visualization and analysis techniques play a key role in the discovery of relevant features in ensemble data. Trends, in the form of persisting commonalities or differences in time-varying ensemble datasets, constitute one of the most expressive feature types in ensemble analysis. We develop a flow-graph representation as the core of a system designed for the visual analysis of trends in time-varying ensembles.

Modality-Driven Classification and Visualization of Ensemble Variance.

Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.

An Automated Approach for Slicing Plane Placement in Visual Data Analysis.

Effective display and visual analysis of complex 3D data is a challenging task. Occlusions, overlaps, and projective distortions-as frequently caused by typical 3D rendering techniques-can be major obstacles to unambiguous and robust data analysis. Slicing planes are a ubiquitous tool to resolve several of these issues.

Interpolation-Based Pathline Tracing in Particle-Based Flow Visualization.

Particle tracing in time-varying flow fields is traditionally performed by numerical integration of the underlying vector field. This procedure can become computationally expensive, especially in scattered, particle-based flow fields, which complicate interpolation due to the lack of an explicit neighborhood structure. If such a particle-based flow field allows for the identification of consecutive particle positions, an alternative approach to particle tracing can be employed: we substitute repeated numerical integration of vector data by geometric interpolation in the highly dynamic particle system as defined by the particle-based simulation.

Comparative visual analysis of Lagrangian transport in CFD ensembles.

Sets of simulation runs based on parameter and model variation, so-called ensembles, are increasingly used to model physical behaviors whose parameter space is too large or complex to be explored automatically. Visualization plays a key role in conveying important properties in ensembles, such as the degree to which members of the ensemble agree or disagree in their behavior. For ensembles of time-varying vector fields, there are numerous challenges for providing an expressive comparative visualization, among which is the requirement to relate the effect of individual flow divergence to joint transport characteristics of the ensemble.

Characterizing and visualizing predictive uncertainty in numerical ensembles through Bayesian model averaging.

Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g.

Cubic gradient-based material interfaces.

Multifluid simulations often create volume fraction data, representing fluid volumes per region or cell of a fluid data set. Accurate and visually realistic extraction of fluid boundaries is a challenging and essential task for efficient analysis of multifluid data. In this work, we present a new material interface reconstruction method for such volume fraction data.

On mesh-free valley surface extraction with application to low frequency sound simulation.

Crease surfaces describe extremal structures of 3D scalar fields. We present a new region-growing-based approach to the meshless extraction of adaptive nonmanifold valley and ridge surfaces that overcomes limitations of previous approaches by decoupling point seeding and triangulation of the surface. Our method is capable of extracting valley surface skeletons as connected minimum structures.