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.

本篇论文基于 DL 算法在科学可视化方面的应用，围绕域设置、研究任务、学习类型、网络架构、损失函数和评估指标等六个方面，分类和讨论解决标量场和矢量场数据问题的相关工作，为 SciVis 研究者提供了最新前沿综述和未来增长方向的指引。