Search Results for author: Sarunas Verner

Found 7 papers, 0 papers with code

Exploring the Truth and Beauty of Theory Landscapes with Machine Learning

no code implementations21 Jan 2024 Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner

Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters.

Seeking Truth and Beauty in Flavor Physics with Machine Learning

no code implementations31 Oct 2023 Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner

The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' criteria like beauty, naturalness, etc.

Identifying the Group-Theoretic Structure of Machine-Learned Symmetries

no code implementations14 Sep 2023 Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner

We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage.

Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6

no code implementations10 Jul 2023 Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner

Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry generators.

Discovering Sparse Representations of Lie Groups with Machine Learning

no code implementations10 Feb 2023 Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner

Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators.

Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles

no code implementations13 Jan 2023 Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup Unlu, Sarunas Verner

We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset.

Building Models of Inflation in No-Scale Supergravity

no code implementations3 Sep 2020 John Ellis, Marcos A. G. Garcia, Natsumi Nagata, Dimitri V. Nanopoulos, Keith A. Olive, Sarunas Verner

A more detailed study of no-scale supergravity reveals a structure that is closely related to that of $R^2$ modifications of the minimal Einstein-Hilbert action for general relativity, opening avenues for constructing no-scale de Sitter and anti-de Sitter models by combining pairs of Minkowski models, as well as generalizations of the original no-scale Starobinsky models of inflation.

High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Theory

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