61 citations · 106 across the 9 of their papers we have counts for
9 papers
Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network
Kieran Holland, Andreas Ipp, David I. Müller +1
Fixed point lattice actions are designed to have continuum classical properties unaffected by discretization effects and reduced lattice artifacts at the quantum level. They provid…
Fixed point actions from convolutional neural networks
Kieran Holland, Andreas Ipp, David I. Müller +1
Lattice gauge-equivariant convolutional neural networks (L-CNNs) can be used to form arbitrarily shaped Wilson loops and can approximate any gauge-covariant or gauge-invariant func…
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…
Applications of Lattice Gauge Equivariant Neural Networks
Matteo Favoni, Andreas Ipp, David I. Müller
The introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving their performance. In lattice gau…
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…
Lattice gauge equivariant convolutional neural networks
Matteo Favoni, Andreas Ipp, David I. Müller +1
We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems. At the heart of this ne…