Rotation-equivariant graph neural network for learning hadronic SMEFT effects
Article in Physical Review D (April 2024)
Deep Learning and Physics
Springer Singapore
The most recent citing publications are shown below. View all 20 publications that cite this research output on Dimensions.
Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks
Preprint in arXiv (November 2023)
Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks
Article in IEEE Transactions on Pattern Analysis and Machine Intelligence (November 2023)