With data comes uncertainty, which is a widespread and frequent phenomenon in data
science and analysis. The amount of information available to us is growing exponentially …

Over the last decade, ensemble visualization has witnessed a significant development due
to the wide availability of ensemble data, and the increasing visualization needs from a …

The analysis of ocean and atmospheric datasets offers a unique set of challenges to
scientists working in different application areas. These challenges include dealing with …

Study of flow instability in turbine engine compressors is crucial to understand the inception
and evolution of engine stall. Aerodynamics experts have been working on detecting the …

An uncertain (scalar, vector, tensor) field is usually perceived as a discrete random field with
a priori unknown probability distributions. To compute derived probabilities, eg for the …

We present a nonparametric statistical framework for the quantification, analysis, and
propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art …

The size of large-scale scientific datasets created from simulations and computed on
modern supercomputers continues to grow at a fast pace. A daunting challenge is to analyze …

Ubiquitous applications of Deep neural networks (DNNs) in different artificial intelligence
systems have led to their adoption in solving challenging visualization problems in recent …

Distributions are often used to model uncertainty in many scientific datasets. To preserve the
correlation among the spatially sampled grid locations in the dataset, various standard …

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 …