Although supercomputers are becoming increasingly powerful, their components have thus
far not scaled proportionately. Compute power is growing enormously and is enabling finely …

The past decade has witnessed the superior power of deep neural networks (DNNs) in
applications across various domains. However, training a high-quality DNN remains a non …

Today's supercomputing capabilities allow ocean scientists to generate simulation data at
increasingly higher spatial and temporal resolutions. However, I/O bandwidth and data …

We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate
model for parameter space exploration of ensemble simulations that allows high-resolution …

With increasing computing capabilities of modern supercomputers, the size of the data
generated from the scientific simulations is growing rapidly. As a result, application scientists …

We present a novel image-based representation to interactively visualize large and
arbitrarily structured volumetric data. This image-based representation is created from a …

In this paper, we present a novel data structure, called the Mixture Graph. This data structure
allows us to compress, render, and query segmentation histograms. Such histograms arise …

Cosmologists build simulations for the evolution of the universe using different initial
parameters. By exploring the datasets from different simulation runs, cosmologists can …

Cosmological N-body simulation is an important tool for scientists to study the evolution of
the universe. With the increase of computing power, billions of particles of high space-time …

The analysis and visualization of data created from simulations on modern supercomputers
is a daunting challenge because the incredible compute power of modern supercomputers …