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 |
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. |
s-CorrPlot: An Interactive Scatterplot for Exploring Correlation, S. McKenna, M. Meyer, C. Gregg, S. Gerber. In Journal of Computational and Graphical Statistics, 2015. DOI: 10.1080/10618600.2015.1021926 The degree of correlation between variables is used in many data analysis applications as a key measure of interdependence. The most common techniques for exploratory analysis of pairwise correlation in multivariate datasets, like scatterplot matrices and clustered heatmaps, however, do not scale well to large datasets, either computationally or visually. We present a new visualization that is capable of encoding pairwise correlation between hundreds of thousands variables, called the s-CorrPlot. The s-CorrPlot encodes correlation spatially between variables as points on scatterplot using the geometric structure underlying Pearson's correlation. Furthermore, we extend the s-CorrPlot with interactive techniques that enable animation of the scatterplot to new projections of the correlation space, as illustrated in the companion video in Supplemental Materials. We provide the s-CorrPlot as an open-source R-package and validate its effectiveness through a variety of methods including a case study with a biology collaborator. |