Related references
Note: Only part of the references are listed.
Article
Multidisciplinary Sciences
Alexander Roman et al.
Summary: A deep learning methodology is developed to simultaneously discover multiple non-trivial continuous symmetries in a labeled dataset. Symmetry transformations and generators are modeled using fully connected neural networks trained with a specially constructed loss function. The method demonstrates successful identification of symmetries in the data as interpretable non-trivial transformations in a reduced latent space.
Article
Physics, Multidisciplinary
Ziming Liu et al.
Summary: This automated method can find hidden symmetries by quantifying asymmetry and numerically optimizing to minimize violation in all invertible transformations. It has rediscovered hidden symmetries like the Gullstrand-Painlevé metric.
PHYSICAL REVIEW LETTERS
(2022)
Article
Physics, Multidisciplinary
Barry M. Dillon et al.
Summary: This paper introduces the JetCLR method to solve the mapping problem from low-level data to optimized observables through self-supervised contrastive learning. A symmetric data representation is constructed using a permutation-invariant transformer-encoder network for top and QCD jets, and its performance is compared with alternative representations using linear classifier tests, showing good results.
Article
Astronomy & Astrophysics
Sean Craven et al.
Summary: We propose a simple method to identify continuous Lie algebra symmetry in a dataset through regression using an artificial neural network. Our method takes advantage of the scaling of the output variable under infinitesimal symmetry transformations on the input variables and minimizes the possibility of false identification by generating symmetry transformations post-training.
Article
Astronomy & Astrophysics
Krish Desai et al.
Summary: This paper provides a rigorous statistical definition of dataset symmetries and proposes SymmetryGAN, a deep learning method based on generative adversarial networks, for automatically discovering symmetries. It also considers procedures to infer the underlying symmetry group from empirical data.
Article
Physics, Multidisciplinary
Ziming Liu et al.
Summary: AI Poincar6 is a machine learning algorithm that can automatically discover conserved quantities and other properties in unknown dynamical systems. Testing on various Hamiltonian systems shows that it performs well in identifying exact conserved quantities, periodic orbits, phase transitions, and breakdown timescales for approximate conservation laws.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Mathematical
Daniele Corradetti
Summary: Recent papers have revitalized the study of the exceptional Jordan algebra h(3)(O) in its relations with the true Standard Model gauge group G(SM). The lack of complex representations of F-4 excludes Aut(h(3)(O)) from being a candidate for any Grand Unified Theories, but the automorphisms of the complexification of this algebra, i.e., h(3)(C)(O), are isomorphic to the compact form of E-6, leading to the gauge group of the minimal left-right symmetric extension of the Standard Model.
JOURNAL OF GEOMETRY AND SYMMETRY IN PHYSICS
(2021)
Article
Astronomy & Astrophysics
Heng-Yu Chen et al.
Summary: This letter demonstrates how the computation of tensor products and branching rules of irreducible representations can be machine-learnable and achieve significant speed-ups compared to non-ML algorithms.
Article
Physics, Multidisciplinary
Gabriela Barenboim et al.
Summary: Researchers explore whether Neural Networks can spontaneously discover symmetries as they learn tasks, successfully identifying symmetry information in controlled experiments using Physics templates. This method is also applied to recognize symmetry in artistic paintings.
Article
Physics, Multidisciplinary
Raban Iten et al.
PHYSICAL REVIEW LETTERS
(2020)
Review
Physics, Multidisciplinary
Djuna Croon et al.
FRONTIERS IN PHYSICS
(2019)
Article
Physics, Nuclear
Ivan Todorov et al.
INTERNATIONAL JOURNAL OF MODERN PHYSICS A
(2018)
Article
Mathematics, Applied
Ivan Todorov et al.
ADVANCES IN APPLIED CLIFFORD ALGEBRAS
(2018)
Article
Astronomy & Astrophysics
Amir Shahlaei et al.
Proceedings Paper
Physics, Multidisciplinary
Shahnoosh Rafibakhsh et al.
XIITH QUARK CONFINEMENT AND THE HADRON SPECTRUM
(2017)
Article
Computer Science, Interdisciplinary Applications
Thomas Deppisch
COMPUTER PHYSICS COMMUNICATIONS
(2017)
Article
Physics, Mathematical
Fabio Bernardoni et al.
Advances in Theoretical and Mathematical Physics
(2013)
Article
Astronomy & Astrophysics
S. Deldar et al.
Article
Physics, Mathematical
Vittorino Talamini
JOURNAL OF MATHEMATICAL PHYSICS
(2010)
Article
Physics, Mathematical
Fabio Bernardoni et al.
JOURNAL OF MATHEMATICAL PHYSICS
(2008)
Review
Physics, Multidisciplinary
BS Acharya et al.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2004)
