Equivariance Is Not All You Need: Characterizing the Utility of
  Equivariant Graph Neural Networks for Particle Physics Tasks
Savannah Thais Daniel Murnane
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.