In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics.

/pdf/curve-boxplot-generalization-of-boxplot-for-ensembles-of-3gmiie9adc.pdf

Curve Boxplot: Generalization of Boxplot for Ensembles of Curves

In recent years, there is an emerging direction that leverages information theory to solve many challenging problems in scientific data analysis and visualization. In this article, we review the key concepts in information theory, discuss how the principles of information theory can be useful for visualization, and provide specific examples to draw connections between data communication and data visualization in terms of how information can be measured quantitatively. As the amount of digital data available to us increases at an astounding speed, the goal of this article is to introduce the interested readers to this new direction of data analysis research, and to inspire them to identify new applications and seek solutions using information theory.

https://www.mdpi.com/1099-4300/13/1/254/pdf

Information Theory in Scientific Visualization

Flow visualization is a well established branch of scientific visualization and it currently represents an invaluable resource to many fields, like automotive design, meteorology and medical imaging. Thanks to the capabilities of modern hardware, flow datasets are increasing in size and complexity, and traditional flow visualization techniques need to be updated and improved in order to deal with the upcoming challenges. A fairly recent trend to enhance the expressiveness of scientific visualization is to produce depictions of physical phenomena taking inspiration from traditional handcrafted illustrations: this approach is known as illustrative visualization, and it is getting a foothold in flow visualization as well. In this state of the art report we give an overview of the existing illustrative techniques for flow visualization, we highlight which problems have been solved and which issues still need further investigation, and, finally, we provide remarks and insights on the current trends in illustrative flow visualization.

Illustrative Flow Visualization: State of the Art, Trends and Challenges

Effective 3D streamline placement and visualization play an essential role in many science and engineering disciplines The main challenge for effective streamline visualization lies in seed placement, ie, where to drop seeds and how many seeds should be placed Seeding too many or too few streamlines may not reveal flow features and patterns either because it easily leads to visual clutter in rendering or it conveys little information about the flow field Not only does the number of streamlines placed matter, their spatial relationships also play a key role in understanding the flow field Therefore, effective flow visualization requires the streamlines to be placed in the right place and in the right amount This paper introduces hierarchical streamline bundles, a novel approach to simplifying and visualizing 3D flow fields defined on regular grids By placing seeds and generating streamlines according to flow saliency, we produce a set of streamlines that captures important flow features near critical points without enforcing the dense seeding condition We group spatially neighboring and geometrically similar streamlines to construct a hierarchy from which we extract streamline bundles at different levels of detail Streamline bundles highlight multiscale flow features and patterns through clustered yet not cluttered display This selective visualization strategy effectively reduces visual clutter while accentuating visual foci, and therefore is able to convey the desired insight into the flow data

Hierarchical Streamline Bundles

Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used.

/pdf/similarity-measures-for-enhancing-interactive-streamline-554g980skt.pdf

Similarity Measures for Enhancing Interactive Streamline Seeding

This paper introduces a new streamline placement and selection algorithm for 3D vector fields. Instead of considering the problem as a simple feature search in data space, we base our work on the observation that most streamline fields generate a lot of self-occlusion which prevents proper visualization. In order to avoid this issue, we approach the problem in a view-dependent fashion and dynamically determine a set of streamlines which contributes to data understanding without cluttering the view. Since our technique couples flow characteristic criteria and view-dependent streamline selection we are able achieve the best of both worlds: relevant flow description and intelligible, uncluttered pictures. We detail an efficient GPU implementation of our algorithm, show comprehensive visual results on multiple datasets and compare our method with existing flow depiction techniques. Our results show that our technique greatly improves the readability of streamline visualizations on different datasets without requiring user intervention.

/pdf/view-dependent-streamlines-for-3d-vector-fields-4kfjob6b3o.pdf

View-Dependent Streamlines for 3D Vector Fields

As exemplified in a state-of-the-art bridge-foundation-ground model simulation, a suite of new visualization techniques let scientists study seismic waves and interactively investigate and explore their data. In so doing, the techniques further scientific understanding and thus facilitate the development of new methods to protect real-world infrastructures against otherwise devastating earthquakes.

/pdf/visualizing-3d-earthquake-simulation-data-2pzohh5p46.pdf

Visualizing 3D Earthquake Simulation Data

In this paper, we present a high quality and interactive method for volume rendering curvilinear-grid data sets. This method is based on a two-stage parallel transformation of the sample position into intermediate computational space then into texture space through the use of multiple 1 and 2D deformation textures using hardware acceleration. In this manner, it is possible to render many curvilinear-grid volume data sets at high quality and with a low memory footprint, while taking advantage of modern graphic hardware's tri-linear filtering for the data itself. We also extend our method to handle volume shading. Additionally, we present a comprehensive study and comparisons with previous works, we show improvements both in quality and performance using our technique on multiple curvilinear data sets.

Chris Ho

Papers

View-Dependent Streamlines for 3D Vector Fields

Visualizing 3D Earthquake Simulation Data

Volume Rendering of Curvilinear-Grid Data Using Low-Dimensional Deformation Textures