Abstract
Visualization of large scale time-varying scientific data has been a challenging problem due to their ever-increasing size. Identifying and presenting the most informative (or important) aspects of the data plays an important role in facilitating an efficient visualization. In this paper, an information assisted method is presented to locate temporal and spatial data containing salient physical features and accordingly accelerate the visualization process. Based on information measures, the method adaptively picks up important time steps and sub-regions with the maximum information content so that the time-varying data can be effectively visualized in limited time or using limited resources without loss of potential useful physical features. The experiments on the data of radiation diffusion dynamics and plasma physics simulation demonstrate the effectiveness of the proposed method. The method can remarkably improve the way in which scientists analyze and understand large scale time-varying scientific data
Highlights
With growing capability of supercomputers, scientists are able to simulate sophisticated physical phenomena with ever-increasing scale and accuracy
This is achieved by utilizing several measures from information theory, including Kullback-Leiber distance and off-line marginal utility to detect the time steps that the underline phenomena change most, and normalized entropy to locate where the features exist in a certain time step
The radiation diffusion dynamics and laser simulation data are from scientific simulations which were conducted by scientists at Institute of Applied Physics and Computational Mathematics (IAPCM)
Summary
With growing capability of supercomputers, scientists are able to simulate sophisticated physical phenomena with ever-increasing scale and accuracy. It's necessary to extract data containing interested or important features from these massive scientific time-varying data and allow the scientists to visualize the most salient information in their simulation result without skinning the entire data. We present an information assisted visualization approach to locate and visualize the most informative time steps or sub-regions from time-varying scientific simulation data using information-theoretic measures. This is achieved by utilizing several measures from information theory, including Kullback-Leiber distance and off-line marginal utility to detect the time steps that the underline phenomena change most, and normalized entropy to locate where the features exist in a certain time step.
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