DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
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DOI：
10.1109/tvcg.2022.3167896
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发表时间：
2022-04
影响因子：
5.2
通讯作者：
Chaoli Wang;J. Han
中科院分区：
文献类型：
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作者：
Chaoli Wang;J. Han
自2016年以来，我们见证了人工智能+可视化（AI+VIS）研究的巨大增长。然而，现有的关于AI+VIS的调查文章侧重于可视化分析和信息可视化，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：设计深度学习解决方案来解决SciVis问题。为了保持专注，我们主要考虑处理标量和矢量场数据但不包括网格数据的作品。我们从六个方面对这些工作进行分类和讨论：领域设置、研究任务、学习类型、网络架构、损失函数和评估度量。本文最后讨论了在讨论的维度中需要填补的剩余空白，以及我们作为一个社区需要解决的重大挑战。这项最新的调查指导SciVis研究人员对这一新兴主题进行概述，并指出未来发展这一研究的方向。
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 analytics and information visualization, not scientific visualization (SciVis). In this article, 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 article 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.