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This paper investigates the use of autoencoders and machine learning methods for detecting and analyzing quantum phase transitions in the Two-Component Bose-Hubbard Model. By leveraging deep learning models such as autoencoders, we…

Quantum Gases · Physics 2024-09-30 Iftekher S. Chowdhury , Binay Prakash Akhouri , Shah Haque , Eric Howard

Multi-vehicle interaction behavior classification and analysis offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on…

Robotics · Computer Science 2020-06-16 Wenshuo Wang , Aditya Ramesh , Ding Zhao

The phase-ordering kinetics of the ferromagnetic two-dimensional Ising model with uniform disorder is investigated by intensive Monte Carlo simulations. Taking into account finite-time corrections to scaling, simple ageing behaviour is…

Statistical Mechanics · Physics 2007-09-21 Florian Baumann , Malte Henkel , Michel Pleimling

Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance.…

Computational Engineering, Finance, and Science · Computer Science 2025-01-10 Prakash Thakolkaran , Michael A. Espinal , Somayajulu Dhulipala , Siddhant Kumar , Carlos M. Portela

We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of the system, the critical points can be located exactly for…

Disordered Systems and Neural Networks · Physics 2023-10-31 Anshumitra Baul , Nicholas Walker , Juana Moreno , Ka-Ming Tam

Three 2D spin models made of frustrated zig-zag chains with competing interactions which by exact summation with respect to some degrees of freedom can be replaced by an effective temperature-dependent interaction were considered. The first…

Statistical Mechanics · Physics 2024-03-28 Jozef Sznajd

Machine-learning techniques have proved successful in identifying ordered phases of matter. However, it remains an open question how far they can contribute to the understanding of phases without broken symmetry, such as spin liquids. Here…

Strongly Correlated Electrons · Physics 2019-11-19 Jonas Greitemann , Ke Liu , Ludovic D. C. Jaubert , Han Yan , Nic Shannon , Lode Pollet

We study the phase diagram of the four dimensional Ising model with first and second neighbour couplings, specially in the antiferromagnetic region, by using Mean Field and Monte Carlo methods. From the later, all the transition lines seem…

High Energy Physics - Lattice · Physics 2008-11-26 J. L. Alonso , J. M. Carmona , J. Clemente Gallardo , L. A. Fernandez , D. Iniguez , A. Tarancon , C. L. Ullod

We present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by…

Statistical Mechanics · Physics 2024-12-02 Alexa Tyberg , Yunhao Fan , Gia-Wei Chern

A modern graphics processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two dimensional Ising model [T. Preis et al., J. Comp.…

Computational Physics · Physics 2010-07-22 Benjamin Block , Peter Virnau , Tobias Preis

While the usual goal in Monte Carlo (MC) simulations of Ising models is the efficient generation of spin configurations with Boltzmann probabilities, the inverse problem is to determine the coupling constants from a given set of spin…

Disordered Systems and Neural Networks · Physics 2017-05-24 Joseph Albert , Robert H. Swendsen

With Monte Carlo methods, we investigate the universality class of the depinning transition in the two-dimensional Ising model with quenched random fields. Based on the short-time dynamic approach, we accurately determine the depinning…

Computational Physics · Physics 2012-03-01 X. P. Qin , B. Zheng , N. J. Zhou

In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as…

Materials Science · Physics 2021-05-26 T. Tula , G. Möller , J. Quintanilla , S. R. Giblin , A. D. Hillier , E. E. McCabe , S. Ramos , D. S. Barker , S. Gibson

The purpose of this article is to present a detailed numerical study of the second-order phase transition in the 2D Ising model. The importance of correctly presenting elementary theory of phase transitions, computational algorithms and…

Statistical Mechanics · Physics 2016-10-04 E. Ibarra-García-Padilla , C. G. Malanche-Flores , F. J. Poveda-Cuevas

After a short introduction on frustrated spin systems, we study in this chapter several two-dimensional frustrated Ising spin systems which can be exactly solved by using vertex models. We show that these systems contain most of the…

Statistical Mechanics · Physics 2020-01-01 H. T. Diep , H. Giacomini

Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is…

Quantum Physics · Physics 2020-12-08 Alexander Lidiak , Zhexuan Gong

Using the concept of finite-size scaling, Monte Carlo calculations of various models have become a very useful tool for the study of critical phenomena, with the system linear dimension as a variable. As an example, several recent studies…

Statistical Mechanics · Physics 2009-10-31 Kurt Binder , Erik Luijten , Marcus Müller , Nigel B. Wilding , Henk W. J. Blöte

Identifying quantum phase transitions poses a significant challenge in condensed matter physics, as this requires methods that both provide accurate results and scale well with system size. In this work, we demonstrate how relaxation…

Strongly Correlated Electrons · Physics 2026-02-11 David Jansen , Donato Farina , Luke Mortimer , Timothy Heightman , Andreas Leitherer , Pere Mujal , Jie Wang , Antonio Acín

We analyze the universal features of the critical behaviour of frustrated spin systems with noncollinear order. By means of the field theoretical renormalization group approach, we study the 3d model of a frustrated magnet and obtain…

Statistical Mechanics · Physics 2009-11-10 Yurij Holovatch , Dmytro Ivaneyko , Bertrand Delamotte

We study the 2D Ising model on a square lattice with additional non-equal diagonal next-nearest neighbor interactions. The cases of classical and quantum (transverse) models are considered. Possible phases and their locations in the space…

Statistical Mechanics · Physics 2009-11-10 G. Y. Chitov , C. Gros