15 citations · 31 across the 6 of their papers we have counts for
6 papers
Interactive Visualization of Time-Varying Flow Fields Using Particle Tracing Neural Networks
Mengjiao Han, Jixian Li, Sudhanshu Sane +4
In this paper, we present a comprehensive evaluation to establish a robust and efficient framework for Lagrangian-based particle tracing using deep neural networks (DNNs). Han et a…
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…
A Guide to Particle Advection Performance
Abhishek Yenpure, Sudhanshu Sane, Roba Binyahib +3
The performance of particle advection-based flow visualization techniques is complex, since computational work can vary based on many factors, including number of particles, durati…
Exploratory Lagrangian-Based Particle Tracing Using Deep Learning
Mengjiao Han, Sudhanshu Sane, Chris R. Johnson
Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration.…
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…
Scalable In Situ Lagrangian Flow Map Extraction: Demonstrating the Viability of a Communication-Free Model
Sudhanshu Sane, Abhishek Yenpure, Roxana Bujack +4
We introduce and evaluate a new algorithm for the in situ extraction of Lagrangian flow maps, which we call Boundary Termination Optimization (BTO). Our approach is a communication…