no code implementations • 12 Jan 2024 • Kieran Holland, Andreas Ipp, David I. Müller, Urs Wenger

Fixed point lattice actions are designed to have continuum classical properties unaffected by discretization effects and reduced lattice artifacts at the quantum level.

no code implementations • 1 Dec 2022 • Matteo Favoni, Andreas Ipp, David I. Müller

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).

no code implementations • 23 Dec 2021 • Srinath Bulusu, Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh

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.

no code implementations • 23 Dec 2021 • Srinath Bulusu, Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh

In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories.

no code implementations • 21 Dec 2021 • Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh

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.

1 code implementation • 8 Nov 2021 • Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh

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.

1 code implementation • 26 Mar 2021 • Srinath Bulusu, Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh

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.

1 code implementation • 23 Dec 2020 • Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh

We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems.

1 code implementation • 28 Feb 2017 • Andreas Ipp, David Müller

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

2 code implementations • 23 May 2016 • Daniil Gelfand, Andreas Ipp, David Müller

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

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