The tracking of wind field circulation is an important and difficult problem in typhoon
forecasting. This paper studies how to catch the shape and intensity deformation …

Time-varying volume data is often generated from scientific simulations in a variety of
application domains, such as computational fluid dynamics, combustion science, and …

Effective analysis of features in time-varying data is essential in numerous scientific
applications. Feature extraction and tracking are two important tasks scientists rely upon to …

We introduce a deep learning approach for the identification of shock locations in large
scale tensor field datasets. Such datasets are typically generated by turbulent combustion …

Studying the dynamic evolution of time-varying volumetric data is essential in countless
scientific endeavors. The ability to isolate and track features of interest allows domain …

Interactive visualization of streaming points for real-time scatterplots and linear blending of
correlation patterns is increasingly becoming the dominant mode of visual analytics for both …

Tracking the temporal evolution of features in time-varying data is a key method in
visualization. For typical feature definitions, such as vortices, objects are sparsely distributed …

For large-scale simulations, the data sets are so massive that it is sometimes not feasible to
view the data with basic visualization methods, let alone explore all time steps in detail …

The study of multiphase flow is essential for designing chemical reactors such as fluidized
bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing …

Feature‐based time‐varying volume visualization is combined with illustrative visualization
to tell the story of how mesoscale ocean eddies form in the Gulf Stream and transport heat …