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Related papers: Multiscale Normalizing Flows for Gauge Theories

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The most general SU(3)-singlet space of gauged N=8 supergravity in four-dimensions is studied recently. The SU(3)-invariant six scalar fields are realized by six real four-forms. A family of holographic N=1 supersymmetric RG flows on…

High Energy Physics - Theory · Physics 2014-11-20 Changhyun Ahn

The advantages of using more than one renormalization group (RG) in problems with more than one important length scale are discussed. It is shown that: i) using different RG's can lead to complementary information, i.e. what is very…

High Energy Physics - Theory · Physics 2011-04-15 C. R. Stephens

The Hamiltonian dynamics of a compressible inviscid fluid is formulated as a gauge theory. The idea of gauge equivalence is exploited to unify the study of apparantly distinct physical problems and solutions of new models can be generated…

High Energy Physics - Theory · Physics 2007-05-23 Subir Ghosh

The term higher gauge theory refers to the generalization of gauge theory to a theory of connections at two levels, essentially given by 1- and 2-forms. So far, there have been two approaches to this subject. The differential picture uses…

High Energy Physics - Theory · Physics 2008-11-26 Florian Girelli , Hendryk Pfeiffer

We show that classical U(infinity) gauge theories can be obtained from the dimensional reduction of a certain class of higher-derivative theories. In general, the exact symmetry is attained in the limit of degenerate metric; otherwise, the…

Mathematical Physics · Physics 2013-02-26 Kiyoshi Shiraishi

We investigate the mixing properties of scalars stirred by spatially smooth, divergence-free flows and maintained by a steady source-sink distribution. We focus on the spatial variation of the scalar field, described by the {\it dissipation…

Fluid Dynamics · Physics 2015-05-27 Alexandros Alexakis , Alexandra Tzella

We present a novel generative modeling method called diffusion normalizing flow based on stochastic differential equations (SDEs). The algorithm consists of two neural SDEs: a forward SDE that gradually adds noise to the data to transform…

Machine Learning · Computer Science 2021-10-15 Qinsheng Zhang , Yongxin Chen

Multigrid methods were invented for the solution of discretized partial differential equations in ordered systems. The slowness of traditional algorithms is overcome by updates on various length scales. In this article we discuss…

High Energy Physics - Lattice · Physics 2011-04-15 Thomas Kalkreuter

A new class of multiscale scheme is presented for micro-hydrodynamic problems based on a dual representation of the fluid observables. The hybrid model is first tested against the classical flow between two parallel plates and then applied…

Computational Physics · Physics 2020-06-29 Andrea Montessori , Adriano Tiribocchi , Marco Lauricella , Fabio Bonaccorso , Sauro Succi

Normalizing Flows (NFs) are emerging as a powerful class of generative models, as they not only allow for efficient sampling, but also deliver, by construction, density estimation. They are of great potential usage in High Energy Physics…

Machine Learning · Statistics 2023-03-01 Humberto Reyes-Gonzalez , Riccardo Torre

A gauge field model, which simultaneously has strict local gauge symmetry and contains massive general gauge bosons, is discussed in this paper. The model has SU(N) gauge symmetry. In order to introduce the mass term of gauge fields…

High Energy Physics - Phenomenology · Physics 2007-05-23 Ning Wu

A new symmetry-preserving loop regularization method proposed in \cite{ylw} is further investigated. It is found that its prescription can be understood by introducing a regulating distribution function to the proper-time formalism of…

High Energy Physics - Theory · Physics 2009-11-10 Yue-Liang Wu

In this paper, we explore the algebraic and geometric structures that arise from a procedure we dub "gauging the gauge", which involves the promotion of a certain global, coordinate independent symmetry to a local one. By gauging the global…

High Energy Physics - Theory · Physics 2022-11-17 Hank Chen , Florian Girelli

We study magnetically-charged supersymmetric flow equations in a consistent truncation of gauged $\mathcal{N}\,=\,8$ supergravity in five dimensions. This truncation gives gauged $\mathcal{N}\,=\,2$ supergravity coupled to two vector…

High Energy Physics - Theory · Physics 2018-08-31 Minwoo Suh

Starting from the hypothesis of scaling solutions, the general exact form of the scalar field potential is found. In the case of two fluids, it turns out to be a negative power of hyperbolic sine. In the case of three fluids the analytic…

General Relativity and Quantum Cosmology · Physics 2011-07-19 Claudio Rubano , John D. Barrow

We extend the Standard Model gauge group by a a gauged $U(1)_R$ R-Symmetry or a gauged $U(1)'$. The requirement of cancellation of anomalies is very constraining but can be achieved by adding three or four hidden-sector fields which are…

High Energy Physics - Phenomenology · Physics 2009-10-28 A. Chamseddine , H. Dreiner

We present a method to downscale idealized geophysical fluid simulations using generative models based on diffusion maps. By analyzing the Fourier spectra of images drawn from different data distributions, we show how one can chain together…

Machine Learning · Computer Science 2023-05-04 Tobias Bischoff , Katherine Deck

Guided by idealized but soluble nonrenormalizable models, a nontraditional proposal for the quantization of covariant scalar field theories is advanced, which achieves a term-by-term, divergence-free perturbation analysis of interacting…

High Energy Physics - Theory · Physics 2010-09-21 John R. Klauder

Size segregation in granular flows is a well-known phenomenon: laboratory experiments consistently show that large particles migrate toward silo walls during filling, while smaller particles concentrate near the center. Paradoxically, field…

Soft Condensed Matter · Physics 2025-08-04 Shivakumar Athani , Benjy Marks , François Guillard , Alistair Gillespie , Itai Einav

The computational cost associated with simulating fluid flows can make it infeasible to run many simulations across multiple flow conditions. Building upon concepts from generative modeling, we introduce a new method for learning neural…

Computational Physics · Physics 2019-12-17 Jeremy Morton , Freddie D. Witherden , Mykel J. Kochenderfer