2023/11/06 by S. J. Thais, Thais, Savannah, Daniel Murnane +1 · 1 citation
Computer Science · Materials Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Quantum many-body systems
paper · pdf · doi:10.48550/arxiv.2311.03094
openalex publication_date 2023/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Incorporating inductive biases into ML models is an active area of ML research, especially when ML models are applied to data about the physical world. Equivariant Graph Neural Networks (GNNs) have recently become a popular method for learning from physics data because they directly incorporate the symmetries of the underlying physical system. Drawing from the relevant literature around group equivariant networks, this paper presents a comprehensive evaluation of the proposed benefits of equivariant GNNs by using real-world particle physics reconstruction tasks as an evaluation test-bed. We demonstrate that many of the theoretical benefits generally associated with equivariant networks may not hold for realistic systems and introduce compelling directions for future research that will benefit both the scientific theory of ML and physics applications.