Physical design for driven device of Z-FFR based on Machine Learning-Reference-Cited by-同舟云学术

Physical design for driven device of Z-FFR based on Machine Learning

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 15th International Symposium on Computational Intelligence and Design (ISCID)
language:
Short-container-title:

Author:

Xiong Wenbin¹,Tang Zhangchun²,Liu Pan³,Liu Gaoyang⁴,Qu Fanyu⁵,Gao Qiang¹,Liu ChenCheng⁶

Affiliation:

1. MEE,Nuclear and Radiation Safety Center,Beijing,China

2. University of Electronic Science and Technology of China,School of Mechatronics Engineering,Chendu,China

3. Tianfu Innovation Energy Establishment,Department of Theory,Chendu,China

4. Guangzhou EMU Depot of China Railway Guangzhou Co.,Ltd.,Guangzhou,China

5. China National Nuclear Power Co.,Ltd.,Beijing,China

6. Central South University of Forestry and Technology,Changsha,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10029389/10029392/10029580.pdf?arnumber=10029580

Reference12 articles.

1. Measurements of magneto-Rayleigh–Taylor instability growth during the implosion of initially solid metal liners

2. Physical design of fusion target with edge computing;liu;2022 6th International Conference on Electrical Mechanical and Computer Engineering,0

3. A MAML-based aerodynamic modeling method and system;wang;Invention patent of China,0

4. Physical design of controlled thermonuclear fusion with artificial intelligence and big data;liu;2nd Conference on Controlled Thermonuclear Fusion with Artificial Intelligence Applications,0

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