Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
M. Han, T. M. Athawale, D. Pugmire, and C. R. Johnson
[Preprint (arXiv)]
(accepted at the IEEE VIS 2022, Oklahoma City, USA.)
Abstract
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.
Uncertainty-Aware Scientific Data Visualization for Trusted Decision-Making
T. M. Athawale, D. Pugmire, C. R. Johnson, K. Moreland, D. Lu, J Chen, J. Kress, S. Klasky, and M. Parashar
[Presentation]
(accepted in the ASCR Workshop on Visualization for Scientific Discovery, Decision-Making, & Communication, 2022 .)
Efficient Visualization on Complex Distributed Resources
D. Pugmire, K. Moreland, J. Kress, J. Chen, T. M. Athawale, S. Klasky, and H. Childs
(accepted in the ASCR Workshop on Visualization for Scientific Discovery, Decision-Making, & Communication, 2022 .)
Investigating Multivariate, Vector, and Topological Data Analysis Techniques for Mantle Flow Pattern Visualization
S. Sane, T. M. Athawale, and C. R. Johnson
[Preprint] [Presentation video]
(accepted in the 2021 IEEE SciVis Contest (virtual).)
Abstract
The Earth’s mantle convection data available at the SciVis Contest 2021 is discretized on a curvilinear grid with multivariate data consisting of scalar as well as vector field data and consists of 250 time slices 2 Myr apart. Prior studies of mantle flow patterns, as well as works prioritizing mantle convection visualization, have employed time-varying 2D slices, 3D isosurfaces, or volume rendering for the visualization of univariate scalar fields and glyph-based techniques for flow visualization. We demonstrate the use of feature level- sets (isosurface-based multivariate data visualization technique), attribute-filtered integral curves (streamlines and pathlines), and topological analysis to study mantle flow patterns.
Uncertainty Visualization of the Marching Squares and Marching Cubes Topology Cases
T. M. Athawale, S. Sane, and C. R. Johnson
[Preprint (arXiv)] [BibTex] [Preview video] [Presentation slides] [Presentation Video]
(IEEE VIS 2021, New Orleans, LA, USA (virtual).)
Abstract
Marching squares (MS) and marching cubes (MC) are widely used algorithms for level-set visualization of scientific data. In this paper, we address the challenge of uncertainty visualization of the topology cases of the MS and MC algorithms for uncertain scalar field data sampled on a uniform grid. The visualization of the MS and MC topology cases for uncertain data is challenging due to their exponential nature and a possibility of multiple topology cases per cell of a grid. We propose the topology case count and entropy-based techniques for quantifying uncertainty in the topology cases of the MS and MC algorithms when noise in data is modeled with probability distributions. We demonstrate the applicability of our techniques for independent and correlated uncertainty assumptions. We visualize the quantified topological uncertainty via color mapping proportional to uncertainty, as well as with interactive probability queries in the MS case and entropy isosurfaces in the MC case. We demonstrate the utility of our uncertainty quantification framework in identifying the isovalues exhibiting relatively high topological uncertainty. We illustrate the effectiveness of our techniques via results on synthetic, simulation, and hixel datasets.
Visualizing Interactions Between Solar Photovoltaic Farms and the Atmospheric Boundary Layer
T. M. Athawale*, B. Stanislawski*, S. Sane, and C. R. Johnson
* Both authors contributed equally to the paper
[Preprint] [BibTex] [Presentation slides] [Presentation video (at 24:50)]
(EnergyVis '21 workshop co-located with 12th ACM International Conference on Future Energy Systems (e-Energy' 21), June 28-July 2, 2021, Torino, Italy (virtual). ACM, New York, NY, USA, 5 pages, pp 377-381)
Abstract
The efficiency of solar panels depends on the operating temperature. As the panel temperature rises, efficiency drops. Thus, the solar energy community aims to understand the factors that influence the operating temperature, which include wind speed, wind direction, turbulence, ambient temperature, mounting configuration, and solar cell material. We use high-resolution numerical simulations to model the flow and thermal behavior of idealized solar farms. Because these simulations model such complex behavior, advanced visualization techniques are needed to investigate and understand the results. Here, we present advanced 3D visualizations of numerical simulation results to illustrate the flow and heat transport in an idealized solar farm. The findings can be used to understand how flow behavior influences module temperatures, and vice versa.
Visualization of Uncertain Multivariate Data via Feature Confidence Level-Sets
S. Sane, T. M. Athawale, and C. R. Johnson
[Preprint] [Bibtex] [Preview video] [Presentation slides] [Presentation video (talk# 3)]
(EuroVis 2021-23rd EG/VGTC Conference on Visualization, Zurich, Switzerland (virtual).)
Abstract
Recent advancements in multivariate data visualization have opened new research opportunities for the visualization community. In this paper, we propose an uncertain multivariate data visualization technique called feature confidence level-sets. Conceptually, feature level-sets refer to level-sets of multivariate data. Our proposed technique extends the existing idea of univariate confidence isosurfaces to multivariate feature level-sets. Feature confidence level-sets are computed by considering the trait for a specific feature, a confidence interval, and the distribution of data at each grid point in the domain. Using uncertain multivariate data sets, we demonstrate the utility of the technique to visualize regions with uncertainty in relation to the specific trait or feature, and the ability of the technique to provide secondary feature structure visualization based on uncertainty.
Machine Learning & Information Processing, Proceedings of ICMLIP 2020
Editors: D. Swain, P. K. Pattnaik, T. M. Athawale
[Book link]
Abstract
This book includes selected papers from the 2nd International Conference on Machine Learning and Information Processing (ICMLIP 2020), held at Vardhaman College of Engineering, Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, from November 28 to 29, 2020. It presents the latest developments and technical solutions in the areas of advanced computing and data sciences, covering machine learning, artificial intelligence, human–computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.
Statistical Rendering for Visualization of Red Sea Eddy Simulation Data
T. M. Athawale, A. Entezari, B. Wang, and C. R. Johnson
[Preprint] [Poster (interactive)] [Preview video] [Presentation slides] [Presentation video (VIS 2020: SciVis Contest session, third from the last presentation)]
(Accepted in the 2020 IEEE SciVis Contest)
Abstract
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.
A Statistical Framework for Visualization of Positional Uncertainty in Deep Brain Stimulation Electrodes
T. M. Athawale, K. A. Johnson, C. R. Butson, and C. R. Johnson
[arXiv] [Preprint] [Bibtex] [Poster] [Source code (MATLAB)]
(2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC), Vancouver, BC, Canada, 2019, pp. 54-55.)
Abstract
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.
Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
Efficient Visualization on Complex Distributed Resources
Investigating Multivariate, Vector, and Topological Data Analysis Techniques for Mantle Flow Pattern Visualization
Uncertainty Visualization of the Marching Squares and Marching Cubes Topology Cases
Visualizing Interactions Between Solar Photovoltaic Farms and the Atmospheric Boundary Layer
Visualization of Uncertain Multivariate Data via Feature Confidence Level-Sets
