32 citations · 50 across the 14 of their papers we have counts for
14 papers
Distribution-Agnostic Isocontour Confidence Bounds for Robust Uncertainty Visualization of Scalar Field Data
Timbwaoga A. J. Ouermi, Nina M. Gottschling, Alex Gorczowski +1
Uncertainty visualization has been shown to be pivotal for conveying the reliability of features extracted from scalar fields. Features represented by individual isocontours and me…
REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization
Shanu Saklani, Tushar M. Athawale, Nairita Pal +3
Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act…
Efficient Probabilistic Visualization of Local Divergence of 2D Vector Fields with Independent Gaussian Uncertainty
Timbwaoga A. J. Ouermi, Eric Li, Kenneth Moreland +3
This work focuses on visualizing uncertainty of local divergence of two-dimensional vector fields. Divergence is one of the fundamental attributes of fluid flows, as it can help do…
An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations
Robert Sisneros, Tushar M. Athawale, David Pugmire +1
We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the pro…
Estimation and Visualization of Isosurface Uncertainty from Linear and High-Order Interpolation Methods
Timbwaoga A. J. Ouermi, Jixian Li, Tushar Athawale +1
Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface ex…
Uncertainty-Informed Volume Visualization using Implicit Neural Representation
Shanu Saklani, Chitwan Goel, Shrey Bansal +5
The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive general…
Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models
Tushar M. Athawale, Zhe Wang, David Pugmire +5
This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fund…
A Comparative Study of the Perceptual Sensitivity of Topological Visualizations to Feature Variations
Tushar M. Athawale, Bryan Triana, Tanmay Kotha +2
Color maps are a commonly used visualization technique in which data are mapped to optical properties, e.g., color or opacity. Color maps, however, do not explicitly convey structu…
Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models
Tushar M. Athawale, Chris R. Johnson, Sudhanshu Sane +1
Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for mult…
Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
Mengjiao Han, Tushar M. Athawale, David Pugmire +1
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 study…
Uncertainty Visualization of the Marching Squares and Marching Cubes Topology Cases
Tushar M. Athawale, Sudhanshu Sane, Chris R. Johnson
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 visu…
Statistical Rendering for Visualization of Red Sea Eddy Simulation Data
Tushar M. Athawale, Alireza Entezari, Bei Wang +1
Analyzing the effects of ocean eddies is important in oceanology for gaining insights into transport of energy and biogeochemical particles. We present an application of statistica…
Direct Volume Rendering with Nonparametric Models of Uncertainty
Tushar Athawale, Bo Ma, Elham Sakhaee +2
We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statis…
Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps
Tushar Athawale, Dan Maljovec, Chris R. Johnson +2
Morse complexes are gradient-based topological descriptors with close connections to Morse theory. They are widely applicable in scientific visualization as they serve as important…