Study of the energy deposition of helicon plasmas driven by machine learning algorithms-Reference-Cited by-同舟云学术

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Study of the energy deposition of helicon plasmas driven by machine learning algorithms

Published:2022-10-21 Issue: Volume: Page:
ISSN:0863-1042
Container-title:Contributions to Plasma Physics
language:en
Short-container-title:Contributions to Plasma Physics

Author:

Cheng Yuguo¹²,Xia Guangqing¹²,Yang Xiong³

Affiliation:

1. State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian Liaoning China

2. Key Laboratory of Advanced Technology for Aerospace Vehicles of Liaoning Province, Dalian University of Technology Dalian Liaoning China

3. College of Aerospace Science and Engineering National University of Defense Technology Changsha Hunan China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Condensed Matter Physics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ctpp.202200060

Reference35 articles.

1. Characteristics of inductively coupled plasma (ICP) and helicon plasma in a single-loop antenna

2. Diagnosis of helicon plasma by local OES

3. Permanent Magnet Helicon Source for Ion Propulsion

4. Axial force imparted by a conical radiofrequency magneto-plasma thruster

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting the antenna properties of helicon plasma thrusters using machine learning techniques;Journal of Electric Propulsion;2024-03-05

2. Numerical study on effects of voltage amplitude in CO2 pulsed discharges under Martian conditions by deep neural network;Physics of Plasmas;2024-01-01

3. Special issue: Machine learning methods in plasma physics;Contributions to Plasma Physics;2023-06

4. Optimization of uniformity in plasma ashing process using genetic programming;Journal of Physics D: Applied Physics;2023-05-26

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