Inpainting Computational Fluid Dynamics with Physics-Informed Variational Autoencoder

被引:0
|
作者
Wang, Jiamin [1 ]
Yan, Zhexi [1 ]
Wang, Xiaokun [1 ,2 ,3 ]
Zhang, Yalan [1 ,2 ]
Guo, Yu [4 ]
机构
[1] School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing,100083, China
[2] Shunde Graduate School, University of Science and Technology Beijing, Foshan,528300, China
[3] Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing,100083, China
[4] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing,100083, China
关键词
Compendex;
D O I
10.13190/j.jbupt.2023-305
中图分类号
学科分类号
摘要
Computational fluid dynamics - Flow fields - Variational techniques
引用
收藏
页码:29 / 35
相关论文
共 50 条
  • [31] Physics-informed neural networks for learning fluid flows with symmetry
    Younghyeon Kim
    Hyungyeol Kwak
    Jaewook Nam
    Korean Journal of Chemical Engineering, 2023, 40 : 2119 - 2127
  • [32] Physics-informed neural networks (PINNs) for fluid mechanics: a review
    Cai, Shengze
    Mao, Zhiping
    Wang, Zhicheng
    Yin, Minglang
    Karniadakis, George Em
    ACTA MECHANICA SINICA, 2021, 37 (12) : 1727 - 1738
  • [33] Face Image Inpainting via Variational Autoencoder
    Zhang X.
    Cheng L.
    Bai S.
    Zhang F.
    Sun N.
    Wang Z.
    Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2020, 32 (03): : 401 - 409
  • [34] CosmoVAE: Variational Autoencoder for CMB Image Inpainting
    Yi, Kai
    Guo, Yi
    Fan, Yanan
    Hamann, Jan
    Wang, Yu Guang
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [35] Roll Wear Prediction in Strip Cold Rolling with Physics-Informed Autoencoder and Counterfactual Explanations
    Jakubowski, Jakub
    Stanisz, Przemyslaw
    Bobek, Szymon
    Nalepa, Grzegorz J.
    2022 IEEE 9TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), 2022, : 975 - 984
  • [36] Physics-Informed Graph Capsule Generative Autoencoder for Probabilistic AC Optimal Power Flow
    Saffari, Mohsen
    Khodayar, Mahdi
    Khodayar, Mohammad E.
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2024, 8 (05): : 3382 - 3395
  • [37] Physics-Informed Neural Network for Nonlinear Dynamics in Fiber Optics
    Jiang, Xiaotian
    Wang, Danshi
    Fan, Qirui
    Zhang, Min
    Lu, Chao
    Lau, Alan Pak Tao
    LASER & PHOTONICS REVIEWS, 2022, 16 (09)
  • [38] A Physics-Informed General Convolutional Network for the Computational Modeling of Materials With Damage
    Janssen, Jake A.
    Haikal, Ghadir
    DeCarlo, Erin C.
    Hartnett, Michael J.
    Kirby, Matthew L.
    JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2024, 24 (11)
  • [39] Physics-Informed Deep Learning for Computational Elastodynamics without Labeled Data
    Rao, Chengping
    Sun, Hao
    Liu, Yang
    JOURNAL OF ENGINEERING MECHANICS, 2021, 147 (08)
  • [40] Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning
    Mandl, Luis
    Goswami, Somdatta
    Lambers, Lena
    Ricken, Tim
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2025, 434