Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …

Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …

The reconstruction of the trajectories of charged particles, or track reconstruction, is a key
computational challenge for particle and nuclear physics experiments. While the tuning of …

Temporal networks have been widely used to model real-world complex systems such as
financial systems and e-commerce systems. In a temporal network, the joint neighborhood of …

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct
a comprehensive particle-level view of the event by combining information from the …

The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

Abstract The Exa. TrkX project has applied geometric learning concepts such as metric
learning and graph neural networks to HEP particle tracking. Exa. TrkX's tracking pipeline …

In this article, we present a deep learning method to predict the temporal evolution of
dissipative dynamic systems. We propose using both geometric and thermodynamic …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …