Charles HansenVolume RenderingRay Tracing Graphics |
Valerio PascucciTopological MethodsData Streaming Big Data |
Chris JohnsonScalar, Vector, andTensor Field Visualization, Uncertainty Visualization |
Mike KirbyUncertainty Visualization |
Ross WhitakerTopological MethodsUncertainty Visualization |
Miriah MeyerInformation Visualization |
Yarden LivnatInformation Visualization |
Alex LexInformation Visualization |
An Introduction to Verification of Visualization Techniques T. Etiene, R.M. Kirby, C. Silva. Morgan & Claypool Publishers, 2015. |
Visualization of Scalar and Vector Fields C.R. Johnson. In Encyclopedia of Applied and Computational Mathematics, Edited by Björn Engquist, Springer, pp. 1537-1546. 2015. ISBN: 978-3-540-70528-4 |
View-Dependent Streamline Deformation and Exploration X. Tong, J. Edwards, C. Chen, H. Shen, C. R. Johnson, P. Wong. In Transactions on Visualization and Computer Graphics, Vol. PP, No. 99, IEEE, pp. 1-1. 2015. ISSN: 1077-2626 DOI: 10.1109/TVCG.2015.2502583 Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely. Keywords: Context;Deformable models;Lenses;Shape;Streaming media;Three-dimensional displays;Visualization;Flow visualization;deformation;focus+context;occlusion;streamline;white matter tracts |
Data Science: What Is It and How Is It Taught?, H. De Sterck, C.R. Johnson. In SIAM News, SIAM, July, 2015. |
Visualization C.R. Johnson, K. Potter. In The Princeton Companion to Applied Mathematics, Edited by Nicholas J. Higham, Princeton Unicersity Press, pp. 843-846. September, 2015. ISBN: 9780691150390 |
Morse-Smale Analysis of Ion Diffusion for DFT Battery Materials Simulations, A. Gyulassy, A. Knoll, K. C. Lau, Bei Wang, P. T. Bremer, M. E. Papka, L. A. Curtiss, V. Pascucci. Topology-Based Methods in Visualization (TopoInVis), 2015. Ab initio molecular dynamics (AIMD) simulations are increasingly useful in modeling, optimizing and synthesizing materials in energy sciences. In solving Schrodinger's equation, they generate the electronic structure of the simulated atoms as a scalar field. However, methods for analyzing these volume data are not yet common in molecular visualization. The Morse-Smale complex is a proven, versatile tool for topological analysis of scalar fields. In this paper, we apply the discrete Morse-Smale complex to analysis of first-principles battery materials simulations. We consider a carbon nanosphere structure used in battery materials research, and employ Morse-Smale decomposition to determine the possible lithium ion diffusion paths within that structure. Our approach is novel in that it uses the wavefunction itself as opposed distance fields, and that we analyze the 1-skeleton of the Morse-Smale complex to reconstruct our diffusion paths. Furthermore, it is the first application where specific motifs in the graph structure of the complete 1-skeleton define features, namely carbon rings with specific valence. We compare our analysis of DFT data with that of a distance field approximation, and discuss implications on larger classical molecular dynamics simulations. |