2022 Review of Data-Driven Plasma Science

TL;DR

This review discusses recent advances in data-driven plasma science, emphasizing how machine learning and data analysis are transforming the understanding and application of plasma phenomena across various fields.

Contribution

It provides a comprehensive overview of the latest developments in data-driven plasma science, highlighting the integration of data science with plasma research and its future potential.

Findings

Data science tools are increasingly applied to plasma data analysis.

Machine learning enables more efficient interpretation of experimental and computational plasma data.

The field is still emerging but shows significant promise for future breakthroughs.

Abstract

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.