Solution for incompressible gas dispersion in an electric thruster discharge cavity based on physics-informed neural networks

WU Meiqi; ZHENG Jinxing; LIU Haiyang; LU Yudong; KE Maolin; DU Yifan; WU Tao; SHI Jiaming

doi:10.3724/j.0253-3219.2026.hjs.49.250152

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Solution for incompressible gas dispersion in an electric thruster discharge cavity based on physics-informed neural networks

NUCLEAR PHYSICS, INTERDISCIPLINARY RESEARCH | 更新时间：2026-03-25

- Solution for incompressible gas dispersion in an electric thruster discharge cavity based on physics-informed neural networks
- NUCLEAR TECHNIQUES Vol. 49, Issue 3, Article number: 030502(2026)
- 作者机构：
  
  1.中国科学技术大学合肥 230026
  2.中国科学院合肥物质科学研究院等离子体物理研究所合肥 230031
- 作者简介：
  
  WU Meiqi, male, born in 1997, graduated from Shihezi University in 2019, master student, focusing on physics-informed neural networks in electric propulsion technology applications
  ZHENG Jinxing, E-mail: jxzheng@ipp.ac.cn
- 基金信息：
  
  Key Project of National Natural Science Foundation of China(52437001);the Key Research Program of Chinese Academy of Sciences(KGFZD-145-23-53)
- DOI：10.3724/j.0253-3219.2026.hjs.49.250152
  CLC： TP39
- Received：03 April 2025，
  
  Revised：2025-05-06，
  
  Published：15 March 2026
- 稿件说明：
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吴梅起,郑金星,刘海洋等.基于物理信息神经网络求解电推进器放电腔中不可压缩气体弥散问题[J].核技术,2026,49(03):030502.

WU Meiqi,ZHENG Jinxing,LIU Haiyang,et al.Solution for incompressible gas dispersion in an electric thruster discharge cavity based on physics-informed neural networks[J].NUCLEAR TECHNIQUES,2026,49(03):030502.
吴梅起,郑金星,刘海洋等.基于物理信息神经网络求解电推进器放电腔中不可压缩气体弥散问题[J].核技术,2026,49(03):030502. DOI： 10.3724/j.0253-3219.2026.hjs.49.250152. CSTR： 32193.14.hjs.CN31-1342/TL.2026.49.250152.

WU Meiqi,ZHENG Jinxing,LIU Haiyang,et al.Solution for incompressible gas dispersion in an electric thruster discharge cavity based on physics-informed neural networks[J].NUCLEAR TECHNIQUES,2026,49(03):030502. DOI： 10.3724/j.0253-3219.2026.hjs.49.250152. CSTR： 32193.14.hjs.CN31-1342/TL.2026.49.250152.

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Solution for incompressible gas dispersion in an electric thruster discharge cavity based on physics-informed neural networks

DOI：10.3724/j.0253-3219.2026.hjs.49.250152