The ability to identify and present the most essential aspects of time-varying data is critically
important in many areas of science and engineering. This paper introduces an importance …

We present TSR-TVD, a novel deep learning framework that generates temporal super-
resolution (TSR) of time-varying data (TVD) using adversarial learning. TSR-TVD is the first …

In this state‐of‐the‐art report we discuss relevant research works related to the visualization
of complex, multi‐variate data. We discuss how different techniques take effect at specific …

Graphics artists commonly employ physically-based simulation for the generation of effects
such as smoke, explosions, and similar phenomena. The task of finding the correct …

Great advancements in commodity graphics hardware have favoured graphics processing
unit (GPU)‐based volume rendering as the main adopted solution for interactive exploration …

Exploring and analyzing the temporal evolution of features in large-scale time-varying
datasets is a common problem in many areas of science and engineering. One natural …

A new type of glyph is introduced to visualize unsteady flow with static images, allowing
easier analysis of time-dependent phenomena compared to animated visualization …

Time-varying, multi-variate, and comparative data sets are not easily visualized due to the
amount of data that is presented to the user at once. By combining several volumes together …

Traditional deep learning algorithms assume that all data is available during training, which
presents challenges when handling large-scale time-varying data. To address this issue, we …

Animation is an effective way to show how time-varying phenomena evolve over time. A key
issue of generating a good animation is to select ideal views through which the user can …