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pro vyhledávání: '"Favoni, Matteo"'
The introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving their performance. In lattice gauge theories, such information can be identified with gauge symmetries,
Externí odkaz:
http://arxiv.org/abs/2212.00832
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. In
Externí odkaz:
http://arxiv.org/abs/2112.12493
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 symmetries, whose inclusion in the neural network properties can lead to hi
Externí odkaz:
http://arxiv.org/abs/2112.12474
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. We review aspects of the architecture and s
Externí odkaz:
http://arxiv.org/abs/2112.11239
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. We discu
Externí odkaz:
http://arxiv.org/abs/2111.04389
Publikováno v:
Phys. Rev. D 104, 074504 (2021)
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 draw
Externí odkaz:
http://arxiv.org/abs/2103.14686
Publikováno v:
Phys.Rev.Lett. 128 (2022) 3, 032003
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 network structure is a novel convolutional layer that preserves gauge eq
Externí odkaz:
http://arxiv.org/abs/2012.12901
Autor:
Favoni, Matteo1 favoni@hep.itp.tuwien.ac.at, Ipp, Andreas1 ipp@hep.itp.tuwien.ac.at, Müller, David I.1,2 dmueller@hep.itp.tuwien.ac.at, Schuh, Daniel1 schuh@hep.itp.tuwien.ac.at
Publikováno v:
EPJ Web of Conferences. 1/18/2022, Vol. 258, p1-8. 8p.
Autor:
Bulusu, Srinath1 sbulusu@hep.itp.tuwien.ac.at, Favoni, Matteo1,2 favoni@hep.itp.tuwien.ac.at, Ipp, Andreas1 ipp@hep.itp.tuwien.ac.at, Müller, David I.1 dmueller@hep.itp.tuwien.ac.at, Schuh, Daniel1 schuh@hep.itp.tuwien.ac.at
Publikováno v:
EPJ Web of Conferences. 1/18/2022, Vol. 258, p1-8. 8p.
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