Special Issue: Physics-Informed Machine Learning for Advanced Manufacturing

被引:0
|
作者
Guo, Yuebin [1 ]
机构
[1] Rutgers Univ New Brunswick, Dept Mech & Aerosp Engn, Piscataway, NJ 08854 USA
来源
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME | 2024年 / 146卷 / 08期
关键词
D O I
10.1115/1.4065694
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
引用
收藏
页数:1
相关论文
共 50 条
  • [1] Guest Editorial: Special Issue on Physics-Informed Machine Learning
    Piccialli, Francesco
    Raissi, Maizar
    Viana, Felipe A. C.
    Fortino, Giancarlo
    Lu, Huimin
    Hussain, Amir
    IEEE Transactions on Artificial Intelligence, 2024, 5 (03): : 964 - 966
  • [2] Physics-Informed Machine Learning for metal additive manufacturing
    Farrag, Abdelrahman
    Yang, Yuxin
    Cao, Nieqing
    Won, Daehan
    Jin, Yu
    PROGRESS IN ADDITIVE MANUFACTURING, 2025, 10 (01) : 171 - 185
  • [3] Physics-informed machine learning
    George Em Karniadakis
    Ioannis G. Kevrekidis
    Lu Lu
    Paris Perdikaris
    Sifan Wang
    Liu Yang
    Nature Reviews Physics, 2021, 3 : 422 - 440
  • [4] Physics-informed machine learning
    Karniadakis, George Em
    Kevrekidis, Ioannis G.
    Lu, Lu
    Perdikaris, Paris
    Wang, Sifan
    Yang, Liu
    NATURE REVIEWS PHYSICS, 2021, 3 (06) : 422 - 440
  • [5] A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications
    Zobeiry, Navid
    Humfeld, Keith D.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2021, 101
  • [6] 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
  • [7] Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects
    Du, Y.
    Mukherjee, T.
    DebRoy, T.
    APPLIED MATERIALS TODAY, 2021, 24
  • [8] Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning
    Wenzel, Soren
    Slomski-Vetter, Elena
    Melz, Tobias
    MACHINES, 2022, 10 (07)
  • [9] Editorial: Special issue on Physics-informed machine learning enabling fault feature extraction and robust failure prognosis
    Hu, Chao
    Goebel, Kai
    Howey, David
    Peng, Zhike
    Wang, Dong
    Wang, Peng
    Youn, Byeng D.
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 192
  • [10] A Taxonomic Survey of Physics-Informed Machine Learning
    Pateras, Joseph
    Rana, Pratip
    Ghosh, Preetam
    APPLIED SCIENCES-BASEL, 2023, 13 (12):