61 citations · 200 across the 11 of their papers we have counts for
11 papers
Geometrical aspects of lattice gauge equivariant convolutional neural networks
Jimmy Aronsson, David I. Müller, Daniel Schuh
Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for convolutional neural networks that can be applied to non-Abelian lattice gauge theories without…
Equivariance and generalization in neural networks
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
The crucial role played by the underlying symmetries of high energy physics and lattice field theories calls for the implementation of such symmetries in the neural network archite…
Generalization capabilities of neural networks in lattice applications
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories. An essential element of such theories is represented by symme…
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…