With the development of deep learning (DL) techniques, many tasks in flow visualization that
used to rely on complex analysis algorithms now can be replaced by DL methods. We …

Synthesis of systems containing application-specific as well as reprogrammable
components, such as off-the-shelf microprocessors, provides a promising approach to …

The objective of this paper is to understand transport behavior in uncertain time-varying flow
fields by redefining the finite-time Lyapunov exponent (FTLE) and Lagrangian coherent …

Interactive visualization and exploration of large spatiotemporal data sets is difficult without
carefully-designed data preprocessing and management tools. We propose a novel …

In this work, we present a novel access pattern estimation approach for parallel particle
tracing in flow field visualization based on deep neural networks. With strong generalization …

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel
particle tracing performance in distributed-memory systems. Our method combines three …

We present a novel high-order access dependencies-based model for efficient pathline
computation in unsteady flow visualization. By taking longer access sequences into account …

Particle tracing is a very important method in flow field data visualization and analysis. By
placing particle seeds in the flow domain and tracing the trajectory of each particle, users …

We present an efficient and scalable solution to estimate uncertain transport behaviors-
stochastic flow maps (SFMs)-for visualizing and analyzing uncertain unsteady flows …

In this paper, we propose a clustering-driven immersive tool named iTDW to build virtual
tiled display wall (TDW) to better present time-varying scientific data. It enables users to …