论文标题
DL4Scivis：一项有关科学可视化深度学习的最新调查
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
论文作者
论文摘要
自2016年以来，我们目睹了人工智能+可视化（AI+VIS）研究的巨大增长。但是，有关AI+VIV的现有调查论文专注于视觉分析和信息可视化，而不是科学可视化（Scivis）。在本文中，我们调查了相关的深度学习（DL）在Scivis中的工作，特别是在DL4Scivis的方向：设计用于解决Scivis问题的DL解决方案。为了保持专注，我们主要考虑处理标量和矢量字段数据但排除网格数据的作品。我们沿六个维度进行分类和讨论这些作品：域设置，研究任务，学习类型，网络体系结构，损失功能和评估指标。本文最后讨论了剩余的空白，以填补讨论的维度以及我们作为一个社区所需的巨大挑战。这项最先进的调查指导了Scivis研究人员概述了这个新兴主题，并指出了未来发展这项研究的方向。
Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.
