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Found 10 papers, 5 papers with code
Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network
Fixed point lattice actions are designed to have continuum classical properties unaffected by discretization effects and reduced lattice artifacts at the quantum level.
Applications of Lattice Gauge Equivariant Neural Networks
In lattice gauge theories, such information can be identified with gauge symmetries, which are incorporated into the network layers of our recently proposed Lattice Gauge Equivariant Convolutional Neural Networks (L-CNNs).
Equivariance and generalization in neural networks
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 architectures that are applied to the physical system under consideration.
Generalization capabilities of neural networks in lattice applications
In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories.
Preserving gauge invariance in neural networks
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 preserving gauge symmetry.
Lattice gauge symmetry in neural networks
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 lattice gauge theory while exactly preserving gauge symmetry.
Generalization capabilities of translationally equivariant neural networks
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, which, when applied to problems in physics, can lead to significant drawbacks in terms of performance and generalizability.
Lattice gauge equivariant convolutional neural networks
We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems.
Broken boost invariance in the Glasma via finite nuclei thickness
We simulate the creation and evolution of non-boost-invariant Glasma in the early stages of heavy ion collisions within the color glass condensate framework.
High Energy Physics - Phenomenology Nuclear Theory
Simulating collisions of thick nuclei in the color glass condensate framework
We present our work on the simulation of the early stages of heavy-ion collisions with finite longitudinal thickness in the laboratory frame in 3+1 dimensions.
High Energy Physics - Phenomenology Nuclear Theory
