We introduce Neural Flow Maps, a novel simulation method bridging the emerging
paradigm of implicit neural representations with fluid simulation based on the theory of flow …

We present NeRVI, a new deep-learning approach that compresses a large collection of
visualization images generated from time-varying data for communicating volume …

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

The finite-time Lyapunov exponent (FTLE) is widely used for understanding the Lagrangian
behavior of unsteady flow fields. The FTLE field contains many important fine-level …

The increasing adoption of Deep Neural Networks (DNNs) has led to their application in
many challenging scientific visualization tasks. While advanced DNNs offer impressive …

可视化与可视分析已成为众多领域中结合人类智能与机器智能协同理解, 分析数据的常见手段.
人工智能可以通过对大数据的学习分析提高数据质量, 捕捉关键信息, 并选取最有效的视觉呈现 …

Lagrangian representations of flow fields have gained prominence for enabling fast,
accurate analysis and exploration of time-varying flow behaviors. In this paper, we present a …

The motion in time-dependent fluid flows is governed by Lagrangian coherent structures
(LCS). One common approach to visualize hyperbolic LCS is to extract and visualize the …

1.1 Background Medical image analysis is a significant application of computer science
aimed at automatically extracting clinically relevant information from images for biological …

Particle tracing is a very important method for scientific visualization of vector fields, but it is
computationally expensive. Deep learning can be used to speed up particle tracing, but …