2022 Review of Data-Driven Plasma Science
Abstrak
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.
Topik & Kata Kunci
Penulis (63)
R. Anirudh
Rick Archibald
M. Salman Asif
M. Becker
S. Benkadda
P. Bremer
Rick H.S. Bud'e
C. Chang
Lei Chen
R. Churchill
J. Citrin
J. Gaffney
Ana Gainaru
W. Gekelman
Tom Gibbs
S. Hamaguchi
Christian Hill
K. Humbird
S. Jalas
S. Kawaguchi
Gon-Ho Kim
M. Kirchen
S. Klasky
J. Kline
Karl Krushelnick
B. Kustowski
G. Lapenta
Wenting Li
T. Ma
Nigel J. Mason
A. Mesbah
C. Michoski
T. Munson
I. Murakami
H. Najm
K. Olofsson
Seolhye Park
J. L. Peterson
Michael Probst
D. Pugmire
B. Sammuli
K. Sawlani
A. Scheinker
D. Schissel
Rob J. Shalloo
Jun Shinagawa
Jaegu Seong
B. Spears
J. Tennyson
J. Thiagarajan
Catalin M. Ticocs
J. Trieschmann
J. Dijk
B. V. Essen
P. Ventzek
Haimin Wang
J. T. Wang
Zhehui Wang
K. Wende
Xueqiao Xu
Hiroshi Yamada
T. Yokoyama
Xinhua Zhang
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 70×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/TPS.2023.3268170
- Akses
- Open Access ✓