Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Johnson, Chris R."'
Autor:
Ouermi, Timbwaoga A. J., Li, Jixian, Morrow, Zachary, Waanders, Bart van Bloemen, Johnson, Chris R.
Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance the understanding and interpretability of vector field data. For instanc
Externí odkaz:
http://arxiv.org/abs/2409.00042
Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization. Although linear interpolation is easy to i
Externí odkaz:
http://arxiv.org/abs/2409.00043
Autor:
Athawale, Tushar M., Wang, Zhe, Pugmire, David, Moreland, Kenneth, Gong, Qian, Klasky, Scott, Johnson, Chris R., Rosen, Paul
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 fundamental topological descriptors used in the visualization and analysis
Externí odkaz:
http://arxiv.org/abs/2407.18015
Autor:
Han, Mengjiao, Li, Jixian, Sane, Sudhanshu, Gupta, Shubham, Wang, Bei, Petruzza, Steve, Johnson, Chris R.
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 al. (2021) first proposed a DNN-based approach to learn Lagrangian repr
Externí odkaz:
http://arxiv.org/abs/2312.14973
In inverse problems, one attempts to infer spatially variable functions from indirect measurements of a system. To practitioners of inverse problems, the concept of "information" is familiar when discussing key questions such as which parts of the fu
Externí odkaz:
http://arxiv.org/abs/2208.09095
Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set vis
Externí odkaz:
http://arxiv.org/abs/2207.11318
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 uncertain
Externí odkaz:
http://arxiv.org/abs/2207.07260
Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian representations have
Externí odkaz:
http://arxiv.org/abs/2110.08338
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 uncert
Externí odkaz:
http://arxiv.org/abs/2108.03066
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 statistical visualization algorithms for the analysis of the Red Sea eddy simula
Externí odkaz:
http://arxiv.org/abs/2106.12138