Comparative Visualization of Ensembles Using Ensemble Surface Slicing

Abstract

By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call Ensemble Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.

Keywords: Ensemble visualization; comparative visualization; isosurface; uncertainty visualization.

Figures

Figure 1

Spot the difference puzzle: There are at least four feature differences between these two images. Side-by-side visualization does not pre-attentively draw interest to areas of difference between two images, thus requiring a linear scanning of all potential features in each image. Scientists viewing side–by–side displays of feature-rich surfaces from ensembles face the same challenge.

Figure 2

(a) Communicating surface shape using principal-direction-driven 3D LIC stroke texture. (b) Weigle’s technique for visualizing nested surfaces by rendering the interior surface opaque with nearest-approach curves dropped from opaque cross-glyphs on the exterior surface. (c) Rheingans surface retiling technique

Figure 3

A banana, apple, grape, and orange shown using ESS. Luminance discontinuities between strips draws attention to areas of difference between neighboring surfaces.

Figure 4

Four gaussian blob surfaces: Viewing these surfaces side–by–side requires saccading between all four views to try to locate the region of difference. ESS simplifies this visual search by using luminance discontinuity to draw attention to discontinuities/buckling on the surface. Localized similarity is characterized by adjacent continuous color bands.In the combined view, viewers can more rapidly see that three of the surfaces are the same, with the orange one differing.

Figure 5

Hydro-physics Fluid Simulation Side–by–side vs. ESS: 4 energy surfaces where e ∈ [0,3]. Leftmost quad (clockwise from top to bottom): pcasc, urqmd, glauber b0, and glauber b7 surfaces. In the side–by–side, it is easy to see which shapes are the same (e.g. pcasc and urqmd share similar shape while glauber simulation initial conditions results in very different shapes). Due to a lack of a common frame of reference, it is challenging to determine where surfaces cross each other in the side-by-side view. ESS (right image) provides understanding of relative scale, surface orientation, and large and small scale differences.

Figure 6

Hydro-physics Fluid Simulation Side–by–side vs. ESS: 4 energy surfaces where e ∈ [0,3]. In the resulting video, one surface in the simulation disappears for a couple of seconds only to reappear again. Domain scientists used this visualization to determine that the resolution of the grid inadequate. Once modified, visualization of the data provided more consistent results.

Figure 7

Weather Fire Burn Simulation Side–by–side vs. ESS: 4 potential temperature surfaces where t ∈ [287°F,345°F]. The bottom two surfaces in the left quad are almost identical; the other two surfaces (top row of quad), are the most different in the set. The ESS visualization (right image) shows both similarities as well as high/low frequency differences between all surfaces in the ensemble. The large differences near the peak in the upper part of the images are visible in side-by-side visualization, but the subtle changes in location of the features in the lower right is difficult to see. Dark gaps between sets rapidly draw attention to both differences in the ESS view.