This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

Current state-of-the-art segmentation methods often require high-resolution input to attain
the high performance, which pushes the limit of data acquisition and brings large …

Implicit neural networks have demonstrated immense potential in compressing volume data
for visualization. However, despite their advantages, the high costs of training and inference …

Traditional deep learning algorithms assume that all data is available during training, which
presents challenges when handling large-scale time-varying data. To address this issue, we …

Ubiquitous applications of Deep neural networks (DNNs) in different artificial intelligence
systems have led to their adoption in solving challenging visualization problems in recent …

Due to its conceptual simplicity and generality, compressive neural representation has
emerged as a promising alternative to traditional compression methods for managing …

Although many deep-learning-based super-resolution approaches have been proposed in
recent years, because no ground truth is available in the inference stage, few can quantify …

We explore an error‐bounded lossy compression approach for reducing scientific data
associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high …

The widespread use of Deep Neural Networks (DNNs) has recently resulted in their
application to challenging scientific visualization tasks. While advanced DNNs demonstrate …

The reconstruction of high-fidelity flow fields from low-fidelity data has attracted considerable
attention in fluid dynamics but poses many challenges to existing deep learning methods …