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 …

Inspired by the great success of machine learning (ML), researchers have applied ML
techniques to visualizations to achieve a better design, development, and evaluation of …

We present an approach for compressing volumetric scalar fields using implicit neural
representations. Our approach represents a scalar field as a learned function, wherein a …

Since 2016, we have witnessed the tremendous growth of artificial intelligence+
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …

We present STNet, an end-to-end generative framework that synthesizes spatiotemporal
super-resolution volumes with high fidelity for time-varying data. STNet includes two …

We present a state‐of‐the‐art report on time‐dependent flow topology. We survey
representative papers in visualization and provide a taxonomy of existing approaches that …

We prese nt SS R-VFD, ano vel dee p lear ni ng fra me w or kt hat pr od uces co here nt s
patial su per-res ol uti on (SSR) of t hree-di me nsi o nal vect or fiel d data (VFD). SS R-VFD …

We present SSR-TVD, a novel deep learning framework that produces coherent spatial
super-resolution (SSR) of time-varying data (TVD) using adversarial learning. In scientific …

Although deep learning has demonstrated its capability in solving diverse scientific
visualization problems, it still lacks generalization power across different tasks. To address …

We present a differentiable volume rendering solution that provides differentiability of all
continuous parameters of the volume rendering process. This differentiable renderer is used …