Particle advection is the fundamental kernel behind most vector field visualization methods.
Yet, the efficient parallel computation of large amounts of particle traces remains …

Particle advection is an important vector field visualization technique that is difficult to apply
to very large data sets in a distributed setting due to scalability limitations in existing …

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 …

Stream surfaces and streamlines are two popular methods for visualizing three-dimensional
flow fields. While several parallel streamline computation algorithms exist, relatively little …

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

We have designed an asynchronous chip for contactless smart cards. Asynchronous circuits
have two power properties that make them very suitable for contactless devices: low …

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 …

There are multiple algorithms for parallelizing particle advection for scientific visualization
workloads. While many previous studies have contributed to the understanding of individual …

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

Particle advection, a fundamental building block for many flow visualization algorithms, is
very difficult to parallelize efficiently. That said, work requesting is a promising technique to …