Position papers, Contests, and Conference Tutorials

Visualizing the uncertainty of ensemble simulations is challenging
due to the large size and multivariate and temporal features of ensemble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte
Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a
deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate
that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

In this tutorial, our goal is to familiarize the audience with the VTK-m library, an open-source toolkit for visualization and analysis on many-core devices. The visualization community can largely benefit from the VTK-m library via: 1) application of its rich set of high-performance portable visualization algorithms for accelerating visualization research and 2) deployment of new visualization algorithms on different high-performance architectures with VTK-m. The tutorial will cover the usage of VTK-m library for scientific visualization and development with VTK-m.

Analyzing the effects of ocean eddies is important in oceanology for gaining insights into the transport of energy and biogeochemical particles. We present an application of statistical visualization techniques for the analysis of the Red Sea eddy simulation ensemble. Specifically, we demonstrate the applications of statistical volume rendering, statistical summary maps, and statistical level sets to velocity magnitude fields derived from the ensemble for the study of eddy positions. In statistical volume rendering, we model per-voxel data uncertainty using noise densities and study the propagation of uncertainty into the volume rendering pipeline. In the statistical summary maps, we characterize the uncertainty of gradient flow destinations to understand the structural variations of Morse complexes across the ensemble. In statistical level sets, we study the effects on isocontour positions for the Gaussian-distributed uncertainty. We demonstrate the utility of our statistical visualizations for the analysis of eddy positions and their spatial uncertainty, as well as for the exploration of the correlation between temperature and velocity magnitude fields and the study of water surface elevation data.

Deep brain stimulation (DBS) is an FDA-approved neurosurgical procedure for treating patients with movement disorders such as Parkinson's disease. Patient-specific computational modeling and visualization play a key role for efficient surgical and therapeutic decision-making relevant to DBS. The computational models analyze DBS post-operative brain imaging, e.g., computed tomography (CT), to understand the DBS electrode positions within the patient's brain. The DBS stimulation settings for optimal patient response depend upon a physician's knowledge regarding precise electrode positions. The finite resolution of brain imaging, however, restricts our understanding regarding precise DBS electrode positions. In our contribution, we study the problem of the quantification of positional uncertainty in the DBS electrodes caused by the finite resolution of post-operative imaging. We propose a Monte Carlo statistical framework, which takes the advantage of our analytical characterization of the DBS electrode geometry to understand the spatial uncertainty in DBS electrodes. Our statistical framework quantifies the uncertainty in two positional parameters of the DBS electrodes, namely, the longitudinal axis direction and the positions at sub-voxel levels. We interactively visualize quantified uncertainties by employing volume rendering and isosurfaces. We show that the spatial variations in the DBS electrode positions are significant for finite resolution imaging, and interactive visualization can be instrumental for efficient interpretation of the positional variations in the DBS lead.