Atoms (1 paper)
W. Hu, M. Shuaibi, A. Das, S. Goyal, A. Sriram, “Forcenet: A Graph Neural Network for Large-Scale Quantum Calculations”, Preprint, 2021
Particles (5 papers)
Z. Ma, Z. Ye, E. Safdarian, W. Pan, “H-HIGNN: A Scalable Graph Neural Network Framework with Hierarchical Matrix Acceleration for Simulation of Large-Scale Particulate Suspensions”, Preprint, April 2025.
Y. Semlani, M. Relan, K. Ramesh, "PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions", Journal of High Energy Physics, 2024.
Z. Que, M. Loo, H. Fan, M. Pierini, A. Tapper, W. Luk, "Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs" International Conference on Field-Programmable Logic and Applications, FPL 2022, pages 327-333, 2022.
S. Gong, Q. Meng, J. Zhang, H. Qu, C. Li, S. Qian, “An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging”, Journal of High Energy Physics, 2022
X. Ju, S. Farrell, P. Calafiura, D. Murnane, L. Gray, “Graph neural Networks For Particle Reconstruction In High Energy Physics Detectors”, Preprint, 2020.
Mechanics (1 paper)
C. Jiang, N. Chen, “G-Twin: Graph Neural Network-Based Digital Twin for Real-Time and High-Fidelity Structural Health Monitoring for Offshore Wind Turbines”, Marine Structures, 2025.
Electrostatic Physical Problem (1 paper)
M. Chenaud, J. Alves, F. Magoules, “Physics-Informed Graph Convolutional Networks: Towards a Generalized Framework for Complex Geometries”, Preprint, 2023.
Flow Diffusion (1 paper)
X. Zhang, J. Shi, J. Li, X. Huang, F. Xiao, Q. Wang, A. Usmani, G. Chen, “Hydrogen Jet and Diffusion Modeling by Physics-Informed Graph Neural Network”, Renewable and Sustainable Energy Reviews, Volume 207, January 2025.