向量场数据建模研究综述
A Short Review on Data Modelling for Vector Fields
摘要：
事实证明，基于统计原理的机器学习方法在处理各种数据分析和分析任务方面非常成功。传统的数据模型主要考虑独立同分布的数据。最近使用配备有诸如卷积层或跳跃连接之类的有效结构的深度神经网络的端到端建模方案的成功允许扩展到更复杂和结构化的实际数据，例如自然语言、图像、视频等。在应用方面，向量场是经验科学以及信号处理中非常有用的数据类型，例如，使用3D矢量场的3D点云的非参数变换、地球科学中的流体流动建模以及物理场建模。这篇综述文章致力于最近的矢量场计算工具，包括矢量数据表示，空间数据的预测模型，以及在计算机视觉，信号处理和经验科学的应用。
英文摘要：
Machine learning methods based on statistical principles have proven highly successful in dealing with a wide variety of data analysis and analytics tasks. Traditional data models are mostly concerned with independent identically distributed data. The recent success of end-to-end modelling scheme using deep neural networks equipped with effective structures such as convolutional layers or skip connections allows the extension to more sophisticated and structured practical data, such as natural language, images, videos, etc. On the application side, vector fields are an extremely useful type of data in empirical sciences, as well as signal processing, e.g. non-parametric transformations of 3D point clouds using 3D vector fields, the modelling of the fluid flow in earth science, and the modelling of physical fields. This review article is dedicated to recent computational tools of vector fields, including vector data representations, predictive model of spatial data, as well as applications in computer vision, signal processing, and empirical sciences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
