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We present a novel technique for amortized posterior estimation using Normalizing Flows trained with likelihood-weighted importance sampling. This approach allows for the efficient inference of theoretical parameters in high-dimensional…

Machine Learning · Computer Science 2026-02-23 Rajneil Baruah

We study the behaviour of the monopole at finite temperature in the (2+1)-dimensional lattice gauge theory dual to the percolation model; by exploiting the correspondences to statistical systems, we possess powerful tools to evaluate the…

High Energy Physics - Lattice · Physics 2009-02-05 Pietro Giudice , Ferdinando Gliozzi , Stefano Lottini

At fine lattice spacings, Markov chain Monte Carlo simulations of QCD and other gauge theories with or without fermions are plagued by slow modes that give rise to large autocorrelation times. This can lead to simulation runs that are…

High Energy Physics - Lattice · Physics 2024-06-12 Timo Eichhorn , Gianluca Fuwa , Christian Hoelbling , Lukas Varnhorst

We identify a recently proposed shifting operation on classical phase space as a gauge transformation for statistical mechanical microstates. The infinitesimal generators of the continuous gauge group form a non-commutative Lie algebra,…

Statistical Mechanics · Physics 2024-11-20 Johanna Müller , Sophie Hermann , Florian Sammüller , Matthias Schmidt

In this contribution we give an introduction to the foundations and methods of lattice gauge theory. Starting with a brief discussion of the quantum mechanical path integral, we develop the main ingredients of lattice field theory:…

High Energy Physics - Lattice · Physics 2007-05-23 G. Münster , M. Walzl

We investigate singly and doubly charged flux tubes in U(1) lattice gauge theory. By simulating the dually transformed path integral we are able to consider large flux tube lengths, low temperatures, and multiply charged systems without…

High Energy Physics - Lattice · Physics 2008-11-26 Martin Zach , Manfried Faber , Peter Skala

We present an exact Monte Carlo algorithm designed to sample theories where the energy is a sum of many couplings of decreasing strength. Our algorithm, simplified from that of L. Lin et al. hep-lat/9905033, avoids the computation of almost…

High Energy Physics - Lattice · Physics 2009-10-31 T. Bakeyev , Ph. de Forcrand

Flow-based models have proven successful for time-series generation, particularly when defined in lower-dimensional latent spaces that enable efficient sampling. However, how to design latent representations with desirable equivariance…

Machine Learning · Computer Science 2026-02-02 Camilo Carvajal Reyes , Felipe Tobar

Analytic variational techniques for lattice gauge theories based on the Rayleigh-Ritz(RR) method were previously developed for euclidean SU(2) gauge theories in 3 and 4 dimensions. Their extensions to SU(3) gauge theory including…

High Energy Physics - Lattice · Physics 2019-08-15 N. D. Hari Dass , G. Subramoniam

We discuss a class of saddle-point configurations in SU(2) lattice gauge theory in three Euclidean dimensions. These configurations are smooth on the scale of the lattice and have an action density exhibiting localized peaks, as has been…

High Energy Physics - Lattice · Physics 2016-11-03 Robert D. Mawhinney

We apply score-based diffusion models to two-dimensional SU(2) lattice pure gauge theory with the Wilson action, extending recent work on U(1) gauge theories. The SU(2) manifold structure is handled through a quaternion parameterization.…

High Energy Physics - Lattice · Physics 2026-02-24 H. Alharazin , J. Yu. Panteleeva , B. -D. Sun

We present a quantum computational framework for SU(2) lattice gauge theory, leveraging continuous variables instead of discrete qubits to represent the infinite-dimensional Hilbert space of the gauge fields. We consider a ladder as well as…

High Energy Physics - Lattice · Physics 2025-06-24 Victor Ale , Nora M. Bauer , Raghav G. Jha , Felix Ringer , George Siopsis

Fluid flow simulations marshal our most powerful computational resources. In many cases, even this is not enough. Quantum computers provide an opportunity to speed up traditional algorithms for flow simulations. We show that lattice-based…

Normalizing Flows are a promising new class of algorithms for unsupervised learning based on maximum likelihood optimization with change of variables. They offer to learn a factorized component representation for complex nonlinear data and,…

Machine Learning · Computer Science 2020-02-17 Reuben Feinman , Nikhil Parthasarathy

The entanglement entropy of SU(N) lattice gauge theory is studied exactly in 1+1 space-time dimensions and in Migdal-Kadanoff approximation in higher dimensional space. The existence of a non-analytical behavior reminiscent of a phase…

High Energy Physics - Lattice · Physics 2010-01-21 Alexander Velytsky

State-of-the-art simulations of discrete gauge theories are based on Markov chains with local changes in the field space, which however at very fine lattice spacings are notoriously difficult due to separated topological sectors of the…

High Energy Physics - Lattice · Physics 2024-02-20 Jacob Finkenrath

The Glasgow reweighting method is evaluated for SU(2) lattice gauge theory at nonzero \mu and finite T. We establish that the ' overlap problem' of SU(3) measurements, in which the transition points determined from thermodynamic observables…

High Energy Physics - Lattice · Physics 2008-11-26 P. R. Crompton

A novel method to study the bulk thermodynamics in lattice gauge theory is proposed on the basis of the Yang-Mills gradient flow with a fictitious time t. The energy density (epsilon) and the pressure (P) of SU(3) gauge theory at fixed…

High Energy Physics - Lattice · Physics 2015-09-07 Masayuki Asakawa , Tetsuo Hatsuda , Etsuko Itou , Masakiyo Kitazawa , Hiroshi Suzuki

The Hamiltonian limit of lattice gauge theories can be found by extrapolating the results of anisotropic lattice computations, i.e., computations using lattice actions with different temporal and spatial lattice spacings ($a_t\neq a_s$), to…

High Energy Physics - Lattice · Physics 2022-12-20 L. Funcke , C. F. Groß , K. Jansen , S. Kühn , S. Romiti , C. Urbach

Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution of interest. Recent work showed that such generative models…

Machine Learning · Statistics 2020-10-27 Jonas Köhler , Leon Klein , Frank Noé
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