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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
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
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.
Autor:
Bulusu, Srinath
Lattice quantum field theory is a frequently used approach to explore, describe and investigate physical phenomena computationally using numerical simulations. Machine Learning (ML) has the potential to provide useful tools and techniques which may a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ca59b1ecd48d01606ffbe2490c6b8dd