61 citations · 106 across the 9 of their papers we have counts for
4 papers · 1 filter
Preserving gauge invariance in neural networks
Matteo Favoni, Andreas Ipp, David I. Müller +1
In these proceedings we present lattice gauge equivariant convolutional neural networks (L-CNNs) which are able to process data from lattice gauge theory simulations while exactly…
On transverse momentum broadening in real-time lattice simulations of the glasma and in the weak-field limit
Andreas Ipp, David I. Müller, Daniel Schuh
In these proceedings, we report on our numerical lattice simulations of partons traversing the boost-invariant, non-perturbative glasma as created at the early stages of collisions…
Lattice gauge symmetry in neural networks
Matteo Favoni, Andreas Ipp, David I. Müller +1
We review a novel neural network architecture called lattice gauge equivariant convolutional neural networks (L-CNNs), which can be applied to generic machine learning problems in…
Generalization capabilities of translationally equivariant neural networks
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
The rising adoption of machine learning in high energy physics and lattice field theory necessitates the re-evaluation of common methods that are widely used in computer vision, wh…