Scalable algorithms for physics-informed neural and graph networks

被引:0
|
作者
Shukla, Khemraj [1 ]
Xu, Mengjia [1 ,2 ]
Trask, Nathaniel [3 ]
Karniadakis, George E. [1 ]
机构
[1] Division of Applied Mathematics, Brown University, 182 George St, Providence,RI,02912, United States
[2] McGovern Institute for Brain Research, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge,MA,02139, United States
[3] Center for Computing Research, Sandia National Laboratories, 1451 Innovation Pkwy SE #600, Albuquerque,NM,87123, United States
来源
Data-Centric Engineering | 2022年 / 3卷 / 06期
关键词
All Open Access; Gold; Green;
D O I
暂无
中图分类号
学科分类号
摘要
107
引用
收藏
相关论文
共 50 条
  • [1] Scalable algorithms for physics-informed neural and graph networks
    Shukla, Khemraj
    Xu, Mengjia
    Trask, Nathaniel
    Karniadakis, George E.
    DATA-CENTRIC ENGINEERING, 2022, 3
  • [2] Physics-Informed Graph Neural Networks for Water Distribution Systems
    Ashraf, Inaam
    Strotherm, Janine
    Hermes, Luca
    Hammer, Barbara
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20, 2024, : 21905 - 21913
  • [3] Physics-informed and graph neural networks for enhanced inverse analysis
    Di Lorenzo, Daniele
    Champaney, Victor
    Ghnatios, Chady
    Cueto, Elias
    Chinesta, Francisco
    ENGINEERING COMPUTATIONS, 2024,
  • [4] Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks
    Berrone, S.
    Canuto, C.
    Pintore, M.
    Sukumar, N.
    HELIYON, 2023, 9 (08)
  • [5] Physics-Informed Graph Neural Operator for Mean Field Games on Graph: A Scalable Learning Approach
    Chen, Xu
    Liu, Shuo
    Di, Xuan
    GAMES, 2024, 15 (02):
  • [6] Optimal Power Flow With Physics-Informed Typed Graph Neural Networks
    Lopez-Garcia, Tania B.
    Dominguez-Navarro, Jose Antonio
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2025, 40 (01) : 381 - 393
  • [7] Physics-informed heterogeneous graph neural networks for DC blocker placement
    Jin, Hongwei
    Balaprakash, Prasanna
    Zou, Allen
    Ghysels, Pieter
    Krishnapriyan, Aditi S.
    Mate, Adam
    Barnes, Arthur
    Bent, Russell
    ELECTRIC POWER SYSTEMS RESEARCH, 2024, 235
  • [8] Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
    Thangamuthu, Abishek
    Kumar, Gunjan
    Bishnoi, Suresh
    Bhattoo, Ravinder
    Krishnan, N. M. Anoop
    Ranu, Sayan
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
  • [9] Separable Physics-Informed Neural Networks
    Cho, Junwoo
    Nam, Seungtae
    Yang, Hyunmo
    Yun, Seok-Bae
    Hong, Youngjoon
    Park, Eunbyung
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [10] Quantum Physics-Informed Neural Networks
    Trahan, Corey
    Loveland, Mark
    Dent, Samuel
    ENTROPY, 2024, 26 (08)