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With the advent of advanced quantum processors capable of probing lattice gauge theories (LGTs) in higher spatial dimensions, it is crucial to understand string dynamics in such models to guide upcoming experiments and to make connections…

High Energy Physics - Lattice · Physics 2025-07-03 Kaidi Xu , Umberto Borla , Sergej Moroz , Jad C. Halimeh

Traditional theories of optimization cannot describe the dynamics of optimization in deep learning, even in the simple setting of deterministic training. The challenge is that optimizers typically operate in a complex, oscillatory regime…

Machine Learning · Computer Science 2025-09-26 Jeremy M. Cohen , Alex Damian , Ameet Talwalkar , J. Zico Kolter , Jason D. Lee

This paper introduces the Strain Elevation Tension Spring embedding (SETSe) algorithm, a graph embedding method that uses a physics model to create node and edge embeddings in undirected attribute networks. Using a low-dimensional…

Social and Information Networks · Computer Science 2020-07-21 Jonathan Bourne

Motivated by the possibility of an effective string description for the infrared limit of pure Yang-Mills theory, we present a toy model for an effective theory of random surfaces propagating in a target space of $D>2$. We show that the…

High Energy Physics - Theory · Physics 2015-06-26 S. Chaudhuri , D. Minic

Deep generative models have recently garnered significant attention across various fields, from physics to chemistry, where sampling from unnormalized Boltzmann-like distributions represents a fundamental challenge. In particular,…

Machine Learning · Computer Science 2025-06-09 Janik Kreit , Dominic Schuh , Kim A. Nicoli , Lena Funcke

TEK reduction is a well-established technique that allows single-site simulations of Yang-Mills theory in the large-$N_c$ limit by exploiting volume reduction induced by twisted boundary conditions. We performed simulations for $SU(841)$…

High Energy Physics - Lattice · Physics 2023-12-01 Pietro Butti , Antonio Gonzalez-Arroyo

Recent lattice results strongly support the Axionic String Ansatz (ASA) for quantum numbers of glueballs in 3D Yang-Mills theory. The ASA treats glueballs as closed bosonic strings. The corresponding worldsheet theory is a deformation of…

High Energy Physics - Theory · Physics 2023-04-13 Sergei Dubovsky , Guzmán Hernández-Chifflet , Shahrzad Zare

Recently, Nagib et al (2024} utilized indicator functions of profiles of the streamwise normal stress to reveal the ranges of validity, in wall distance and Reynolds number, for each of two proposed models in DNS of channel and pipe flows.…

Fluid Dynamics · Physics 2024-11-20 Hassan Nagib , Ivan Marusic

We use effective string theory to study mesons with large spin $J$ in large $N_c$ QCD as rotating open strings. In the first part of this work, we formulate a consistent effective field theory (EFT) for open spinning strings with light…

High Energy Physics - Theory · Physics 2025-01-15 Gabriel Cuomo , Sergei Dubovsky , Guzmán Hernández-Chifflet , Alexander Monin , Shahrzad Zare

Regular expressions are a classical concept in formal language theory. Regular expressions in programming languages (RegEx) such as JavaScript, feature non-standard semantics of operators (e.g. greedy/lazy Kleene star), as well as…

Time evolution of a circular cosmic string loop is investigated by numerically solving the field equations for the scalar and the gauge fields consisting of the vortex. It is shown that the result agrees with an analytic estimate based on…

Astrophysics · Physics 2009-10-22 Michiyasu Nagasawa , Jun'ichi Yokoyama

I investigate the higher-derivative conformal theory which shows how the Nambu-Goto and Polyakov strings can be told apart. Its energy-momentum tensor is conserved, traceless but does not belong to the conformal family of the unit operator.…

High Energy Physics - Theory · Physics 2023-02-08 Yuri Makeenko

In many scientific applications, the target probability distribution cannot be evaluated in closed form or sampled from directly. Instead, it can often be decomposed into multiple components, some of which are accessible only through…

Methodology · Statistics 2026-03-10 Roxana Darvishi , David C. Stenning , Ted von Hippel , Owen G. Ward

Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations. Due to the invertibility, such models can score unseen data samples…

Machine Learning · Computer Science 2019-06-18 Maximilian Schmidt , Marko Simic

A finite element approach to the elastic flow of a curve coupled with a diffusion equation on the curve is analysed. Considering the graph case, the problem is weakly formulated and approximated with continuous linear finite elements, which…

Numerical Analysis · Mathematics 2017-07-28 Paola Pozzi , Björn Stinner

Stochastic network models play a central role across a wide range of scientific disciplines, and questions of statistical inference arise naturally in this context. In this paper we investigate goodness-of-fit and two-sample testing…

Statistics Theory · Mathematics 2026-03-27 Subhro Ghosh , Rathindra Nath Karmakar , Samriddha Lahiry

Understanding the confinement mechanism in gauge theories and the universality of effective string-like descriptions of gauge flux tubes remains a fundamental challenge in modern physics. We probe string modes of motion with dynamical…

Can standard continuous-time generative models represent distributions whose support is an extremely sparse, globally constrained discrete set? We study this question using completed Sudoku grids as a controlled testbed, treating them as a…

Machine Learning · Computer Science 2026-01-29 Mariia Drozdova

We develop a general method for estimating a finite mixture of non-normalized models. Here, a non-normalized model is defined to be a parametric distribution with an intractable normalization constant. Existing methods for estimating…

Machine Learning · Statistics 2021-09-01 Takeru Matsuda , Aapo Hyvarinen

We present a framework based on the generalized lattice-Boltzmann equation using multiple relaxation times with forcing term for eddy capturing simulation of wall bounded turbulent flows. Due to its flexibility in using disparate relaxation…

Computational Physics · Physics 2009-11-13 Kannan N. Premnath , Martin J. Pattison , Sanjoy Banerjee