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In this chapter we review results on the lattice Schwinger model. In par-ticular, we show how the effect of the anomaly is reproduced on the lattice. We connect these results to recent developments in the field of quantum simulation of…

Quantum Physics · Physics 2025-12-15 Joao C. Pinto Barros , Pierpaolo Fontana , Pasquale Sodano , Andrea Trombettoni

The chiral Schwinger model is formulated in the wilson-fermion formulation on the lattice and then simulated by the complex langevin algorithm. The simulation is done both without and with gauge fixing to the Lorentz gauge for the compact…

High Energy Physics - Lattice · Physics 2009-10-28 T. D. Kieu , P. S. Hawkins

The gradient flow is a valuable tool for the lattice community, with applications from scale-setting to implementing chiral fermions. Here I focus on the gradient flow as a means to suppress power-divergent mixing. Power-divergent mixing…

High Energy Physics - Lattice · Physics 2015-12-02 Christopher Monahan

General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a dramatic reduction in efficiency as the system being studied is driven towards a critical point. Recently, a series of seminal studies suggested that normalizing…

High Energy Physics - Lattice · Physics 2021-11-24 Luigi Del Debbio , Joe Marsh Rossney , Michael Wilson

The flow size distribution is a useful metric for traffic modeling and management. Its estimation based on sampled data, however, is problematic. Previous work has shown that flow sampling (FS) offers enormous statistical benefits over…

Information Theory · Computer Science 2011-06-21 Paul Tune , Darryl Veitch

It has become customary to use a smoothing algorithm called "gradient flow" to fix the lattice spacing in a simulation, through a parameter called $t_0$. It is shown that in order to keep the length $t_0$ fixed with respect to mesonic or…

High Energy Physics - Lattice · Physics 2017-07-04 Thomas DeGrand

Sampling equilibrium distributions is fundamental to statistical mechanics. While flow matching has emerged as scalable state-of-the-art paradigm for generative modeling, its potential for equilibrium sampling in condensed-phase systems…

Computational Physics · Physics 2026-03-31 Emil Hoffmann , Maximilian Schebek , Leon Klein , Frank Noé , Jutta Rogal

The paramagnetic phase of heavy fermion systems is investigated, using a non-perturbative local moment approach to the asymmetric periodic Anderson model within the framework of dynamical mean field theory. The natural focus is on the…

Strongly Correlated Electrons · Physics 2009-11-11 David E Logan , N S Vidhyadhiraja

Simulations in high-energy physics are currently emerging as an application of noisy intermediate-scale quantum (NISQ) computers. In this work, we explore the multi-flavor lattice Schwinger model - a toy model inspired by quantum…

We propose Functional Flow Matching (FFM), a function-space generative model that generalizes the recently-introduced Flow Matching model to operate in infinite-dimensional spaces. Our approach works by first defining a path of probability…

Machine Learning · Computer Science 2023-12-07 Gavin Kerrigan , Giosue Migliorini , Padhraic Smyth

The lattice Boltzmann method has become a standard technique for simulating a wide range of fluid flows. However, the intrinsic coupling of momentum and space discretization restricts the traditional lattice Boltzmann method to regular…

We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics. In contrast to the deep architectures used so far for this task, our proposal is based on a…

Machine Learning · Computer Science 2021-11-29 Pim de Haan , Corrado Rainone , Miranda C. N. Cheng , Roberto Bondesan

Sampling is a fundamental algorithmic task in wide-ranging applications across multiple disciplines such as scientific computing, statistics and machine learning. In this paper, an efficient stochastic Runge-Kutta scheme is proposed to…

Statistics Theory · Mathematics 2026-05-27 Haotian Lin , Xiaojie Wang , Xiaoyan Zhang

This paper presents the first application of quantum generative models to learned latent space representations of computational fluid dynamics (CFD) data. While recent work has explored quantum models for learning statistical properties of…

Machine Learning · Computer Science 2025-12-30 Achraf Hsain , Fouad Mohammed Abbou

We use free energy lattice Boltzmann methods (FRE LBM) to simulate shear and extensional flow of a binary mixture in two and three dimensions. To this end, two classical configurations are digitally twinned, namely a parallel-band device…

Numerical Analysis · Mathematics 2022-12-06 Stephan Simonis , Johannes Nguyen , Samuel J. Avis , Willy Dörfler , Mathias J. Krause

We systematically compare three filtering methods used to extract topological excitations from lattice gauge configurations, namely smearing, Laplace filtering and the filtered fermionic topological charge (with chirally improved fermions).…

In this work, we propose a model for the orientation of inertialess spheroidal particles suspended in turbulent flows. This model consists in a stochastic version of the Jeffery equation that can be included in a statistical Lagrangian…

Fluid Dynamics · Physics 2023-03-31 Lorenzo Campana , Mireille Bossy , Christophe Henry

In this paper, we develop a lattice Boltzmann scheme based on the Vielbein formalism for the study of fluid flows on spherical surfaces. The Vielbein vector field encodes all details related to the geometry of the underlying spherical…

Large superconducting quantum circuits have a number of important applications in quantum computing. Accurately predicting the performance of these devices from first principles is challenging, as it requires solving the many-body…

We describe a method to put chiral gauge theories on the lattice. Our method makes heavy use of the effective action for chiral fermions in the continuum, which is in general complex. As an example we discuss the chiral Schwinger model.

High Energy Physics - Lattice · Physics 2014-11-17 M. Goeckeler , G. Schierholz