4 research outputs found
2022 Review of Data-Driven Plasma Science
Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final Section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary Section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required.</p
2022 Review of Data-Driven Plasma Science
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.Comment: 112 pages (including 700+ references), 44 figures, submitted to IEEE
Transactions on Plasma Science as a part of the IEEE Golden Anniversary
Special Issu
2022 Review of Data-Driven Plasma Science
International audienceData-driven science and technology offer transformative tools and methods to science. This review article highlightsthe latest development and progress in the interdisciplinary fieldof data-driven plasma science (DDPS), i.e., plasma science whoseprogress is driven strongly by data and data analyses. Plasma isconsidered to be the most ubiquitous form of observable matterin the universe. Data associated with plasmas can, therefore,cover extremely large spatial and temporal scales, and oftenprovide essential information for other scientific disciplines.Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a largeamount of data that can no longer be analyzed or interpretedmanually. This trend now necessitates a highly sophisticateduse of high-performance computers for data analyses, makingartificial intelligence and machine learning vital components ofDDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overviewof fundamental data-driven science, five other sections coverwidely studied topics of plasma science and technologies, i.e.,basic plasma physics and laboratory experiments, magneticconfinement fusion, inertial confinement fusion and high-energydensity physics, space and astronomical plasmas, and plasmatechnologies for industrial and other applications. The finalsection before the summary discusses plasma-related databasesthat could significantly contribute to DDPS. Each primary sectionstarts with a brief introduction to the topic, discusses the stateof-the-art developments in the use of data and/or data-scientificapproaches, and presents the summary and outlook. Despite therecent impressive signs of progress, the DDPS is still in its infancy.This article attempts to offer a broad perspective on the development of this field and identify where further innovations arerequired