Fluid simulation on neural flow maps
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 …
paradigm of implicit neural representations with fluid simulation based on the theory of flow …
NeRVI: Compressive neural representation of visualization images for communicating volume visualization results
We present NeRVI, a new deep-learning approach that compresses a large collection of
visualization images generated from time-varying data for communicating volume …
visualization images generated from time-varying data for communicating volume …
Uncertainty-Aware Deep Neural Representations for Visual Analysis of Vector Field Data
A Kumar, S Garg, S Dutta - arXiv preprint arXiv:2407.16119, 2024 - arxiv.org
The widespread use of Deep Neural Networks (DNNs) has recently resulted in their
application to challenging scientific visualization tasks. While advanced DNNs demonstrate …
application to challenging scientific visualization tasks. While advanced DNNs demonstrate …
[HTML][HTML] Neural Monte Carlo rendering of finite-time Lyapunov exponent fields
Y Xi, W Luan, J Tao - Visual Intelligence, 2023 - Springer
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 …
behavior of unsteady flow fields. The FTLE field contains many important fine-level …
Uncertainty-Informed Volume Visualization using Implicit Neural Representation
S Saklani, C Goel, S Bansal, Z Wang, S Dutta… - arXiv preprint arXiv …, 2024 - arxiv.org
The increasing adoption of Deep Neural Networks (DNNs) has led to their application in
many challenging scientific visualization tasks. While advanced DNNs offer impressive …
many challenging scientific visualization tasks. While advanced DNNs offer impressive …
[PDF][PDF] 智能可视化与可视分析
陶钧, 张宇, 陈晴, 刘灿, 陈思明, 袁晓如 - 中国图象图形学报, 2023 - cjig.cn
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 …
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 …
Interactive Visualization of Time-Varying Flow Fields Using Particle Tracing Neural Networks
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 …
accurate analysis and exploration of time-varying flow behaviors. In this paper, we present a …
[PDF][PDF] Out-of-Core Particle Tracing for Monte Carlo Rendering of Finite-Time Lyapunov Exponents.
N Grätz, T Günther - VMV, 2023 - vc.tf.fau.de
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 …
(LCS). One common approach to visualize hyperbolic LCS is to extract and visualize the …
[PDF][PDF] NEW DEEP LEARNING METHODS FOR MEDICAL IMAGE ANALYSIS AND SCIENTIFIC DATA GENERATION AND COMPRESSION
1.1 Background Medical image analysis is a significant application of computer science
aimed at automatically extracting clinically relevant information from images for biological …
aimed at automatically extracting clinically relevant information from images for biological …
Generalizing Deep Learning Methods for Particle Tracing Using Transfer Learning
S Gupta - 2023 - digitalcommons.usu.edu
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 …
computationally expensive. Deep learning can be used to speed up particle tracing, but …