A Short Review on Data Modelling for Vector Fields

TL;DR

This review discusses recent computational tools and models for vector fields, highlighting their applications in sciences, signal processing, and computer vision, emphasizing advances in data representation and predictive modeling.

Contribution

It provides a comprehensive overview of recent methods and tools for vector field data modeling, including representations and predictive models, across various scientific and engineering applications.

Findings

Recent advances in vector data representations

Development of predictive models for spatial vector data

Applications in computer vision, signal processing, and sciences

Abstract

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.