THE APPLICATION OF MODIFIED PHYSICS-INFORMED NEURAL NETWORKS IN RAYLEIGH-TAYLOR INSTABILITY

被引:0
|
作者
Qiu, Rundi [1 ,2 ,3 ,4 ]
Wang, Jingzhu [1 ,2 ,3 ,4 ]
Huang, Renfang [1 ,2 ,3 ,4 ]
Du, Tezhuan [1 ,2 ,3 ,4 ]
Wang, Yiwei [1 ,2 ,3 ,4 ]
Huang, Chenguang [1 ,2 ,3 ,4 ]
机构
[1] Key Laboratory Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing,100190, China
[2] School of Future Technology, University of Chinese Academy of Sciences, Beijing,100049, China
[3] School of Engineering Science, University of Chinese Academy of Sciences, Beijing,100049, China
[4] Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei,230031, China
来源
Lixue Xuebao/Chinese Journal of Theoretical and Applied Mechanics | 2022年 / 54卷 / 08期
关键词
Compendex;
D O I
暂无
中图分类号
学科分类号
摘要
Two phase flow
引用
收藏
页码:2224 / 2234
相关论文
共 16 条
  • [1] Temporal consistency loss for physics-informed neural networks
    Thakur, Sukirt
    Raissi, Maziar
    Mitra, Harsa
    Ardekani, Arezoo M.
    PHYSICS OF FLUIDS, 2024, 36 (07)
  • [2] Application of physics-informed neural networks for self-similar and transient solutions of spontaneous imbibition
    Deng, Lichi
    Pan, Yuewei
    Journal of Petroleum Science and Engineering, 2021, 203
  • [3] Predicting transformer temperature field based on physics-informed neural networks
    Tang, Pengfei
    Zhang, Zhonghao
    Tong, Jie
    Long, Tianhang
    Huang, Can
    Qi, Zihao
    HIGH VOLTAGE, 2024, 9 (04) : 839 - 852
  • [4] Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
    Djeumou, Franck
    Neary, Cyrus
    Goubault, Eric
    Putot, Sylvie
    Topcu, Ufuk
    Proceedings of Machine Learning Research, 2022, 168 : 263 - 277
  • [5] Saturation and postsaturation phenomena of Rayleigh-Taylor instability with adjacent modes
    Institute of Laser Engineering, Osaka University, 2-6 Yamada-oka, Suita, Osaka 565-0871, Japan
    1600, 264041-2640411 (February 2003):
  • [6] Safe Navigation of Autonomous Underwater Vehicles Using Physics-Informed Neural Networks
    Majumder, Rudrashis
    Makam, Rajini
    Mane, Pruthviraj
    BharathwajK.S
    Sundaram, Suresh
    Oceans Conference Record (IEEE), 2024,
  • [7] Robust quantum gates using smooth pulses and physics-informed neural networks
    Gungordu, Utkan
    Kestner, J. P.
    PHYSICAL REVIEW RESEARCH, 2022, 4 (02):
  • [8] WarpPINN: Cine-MR image registration with physics-informed neural networks
    Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
    不详
    不详
    不详
    不详
    不详
    不详
    arXiv,
  • [9] Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations
    Li, Ye
    Chen, Siqi
    Shan, Bin
    Huang, Sheng-Jun
    IJCAI International Joint Conference on Artificial Intelligence, 2024, : 4497 - 4505
  • [10] Sustained oscillating regime in the two-dimensional magnetic Rayleigh-Taylor instability
    Briard, Antoine
    Grea, Benoit-Joseph
    Nguyen, Florian
    PHYSICS OF FLUIDS, 2024, 36 (08)