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 …

When people work together to analyze a data set, they need to organize their findings,
hypotheses, and evidence, share that information with their collaborators, and coordinate …

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 present a novel scheme for progressive rendering in interactive visualization. Static
settings with respect to a certain image quality or frame rate are inherently incapable of …

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

Volumetric depth images (VDI) are a view-dependent representation that combines the high
quality of images with the explorability of 3D fields. By compressing the scalar data along …

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

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

Parallel numerical simulation is a powerful tool used by scientists to study complex
problems. It has been a common practice to save the simulation output to disk and then …