In recent years, deep learning has opened countless research opportunities across many
different disciplines. At present, visualization is mainly applied to explore and explain neural …

This chapter describes Ascent, a production library for in situ visualization and analysis on
exascale architectures. It begins by describing the library's focal points: minimizing …

Time-varying vector fields produced by computational fluid dynamics simulations are often
prohibitively large and pose challenges for accurate interactive analysis and exploration. To …

Streamlines are an extensively utilized flow visualization technique for understanding,
verifying, and exploring computational fluid dynamics simulations. One of the major …

The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is
responsible for developing critical software capabilities that will enable successful execution …

In this paper we present a reconstruction technique for the reduction of unsteady flow data
based on neural representations of time‐varying vector fields. Our approach is motivated by …

Although many types of computational simulations produce time-varying vector fields,
subsequent analysis is often limited to single time slices due to excessive costs. Fortunately …

The computational work to perform particle advection‐based flow visualization techniques
varies based on many factors, including number of particles, duration, and mesh type. In …

In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field
analysis has recently become an active area of research. However, the current literature is …

We extend the method for particle advection that parallelizes over particles to work in an in
situ setting. We then compare our method with the typical method for in situ, parallelizing …