The considerable interest in the high performance computing (HPC) community regarding
analyzing and visualization data without first writing to disk, ie, in situ processing, is due to …

We propose InSituNet, a deep learning based surrogate model to support parameter space
exploration for ensemble simulations that are visualized in situ. In situ visualization …

Data reduction is increasingly being applied to scientific data for numerical simulations,
scientific visualizations and data analyses. It is most often used to lower I/O and storage …

We present NeRVI, a new deep-learning approach that compresses a large collection of
visualization images generated from time-varying data for communicating volume …

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

The growing power and capacity of supercomputers enable scientific simulations at extreme
scale, leading to not only more accurate modeling and greater predictive ability but also …

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

We present a novel technique to generate transformations between arbitrary volumes,
providing both expressive distances and smooth interpolates. In contrast to conventional …

In this work, we propose a novel view-approximation oriented image database generation
approach (VOIDGA) that enables the adequate generation of arbitrary views. Our approach …

Interactively visualizing large finite element simulation data on High-Performance
Computing (HPC) systems poses several difficulties. Some of these relate to unstructured …