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Despite their exceptional flexibility and popularity, the Monte Carlo methods often suffer from slow mixing times for challenging statistical physics problems. We present a general strategy to overcome this difficulty by adopting ideas and…

Computational Physics · Physics 2017-01-06 Li Huang , Lei Wang

We study dynamic behavior of Potts model with invisible states near the first-order phase transition temperature. We focus on melting process starting from the perfect ordered state. This model is regarded as a standard model to analyze…

Statistical Mechanics · Physics 2011-09-30 Shu Tanaka , Ryo Tamura

The critical behavior in the short-time dynamics for the random-bond Potts ferromagnet in two-dimensions is investigated by short-time dynamic Monte Carlo simulations. The numerical calculations show that this dynamic approach can be…

Soft Condensed Matter · Physics 2009-10-31 He-Ping Ying , Kenji Harada

We present a numerically exact steady-state inchworm Monte Carlo method for nonequilibrium quantum impurity models. Rather than propagating an initial state to long times, the method is directly formulated in the steady-state. This…

Strongly Correlated Electrons · Physics 2023-05-17 André Erpenbeck , Emanuel Gull , Guy Cohen

Continuous-time quantum Monte Carlo refers to a class of algorithms designed to sample the thermal distribution of a quantum Hamiltonian through exact expansions of the Boltzmann exponential in terms of stochastic trajectories which are…

Statistical Mechanics · Physics 2024-07-17 Luke Causer , Konstantinos Sfairopoulos , Jamie F. Mair , Juan P. Garrahan

A zero temperature dynamics of Ising spin glasses and ferromagnets on random graphs of finite connectivity is considered, like granular media these systems have an extensive entropy of metastable states. We consider the problem of what…

Statistical Mechanics · Physics 2009-11-07 David S. Dean , Alexandre Lefèvre

The existence of generalized steady states (GSSs) in nonlinear mechanical systems under moderate temporally aperiodic forcing has only been shown recently. Here we derive systematic expansions for such GSSs and construct a numerical…

Dynamical Systems · Mathematics 2026-02-20 Roshan S. Kaundinya , Isabella Thiel , Bálint Kaszás , Shobhit Jain , George Haller

The three-dimensional anisotropic classical XY ferromagnet has been investigated by extensive Monte Carlo simulation using the Metropolis single spin flip algorithm. The magnetization ($M$) and the susceptibility ($\chi$) are measured and…

Statistical Mechanics · Physics 2023-09-26 Olivia Mallick , Muktish Acharyya

Atomistic simulations provide valuable insights into the physical processes governing material behavior. However, their applicability is fundamentally constrained by the limited time scales accessible to brute-force simulations. This…

Computational Physics · Physics 2026-02-16 Michael Kim , Wei Cai

The critical relaxation from the low-temperature ordered state of the three-dimensional fully frustrated Ising model on a simple cubic lattice has been studied using the short time dynamics method. Particles with the periodic boundary…

Statistical Mechanics · Physics 2016-11-10 V. A. Mutailamov , A. K. Murtazaev

Using Monte Carlo simulations we study the phase ordering dynamics of a \textit{multi}-species system modeled via the prototype $q$-state Potts model. In such a \textit{multi}-species system, we identify a spin states or species as the…

Statistical Mechanics · Physics 2023-03-29 Suman Majumder

Detailed mean field and Monte Carlo studies of the dynamic magnetization-reversal transition in the Ising model in its ordered phase under a competing external magnetic field of finite duration have been presented here. Approximate…

Statistical Mechanics · Physics 2009-10-31 Arkajyoti Misra , Bikas K Chakrabarti

In this article, we present an event-driven algorithm that generalizes the recent hard-sphere event-chain Monte Carlo method without introducing discretizations in time or in space. A factorization of the Metropolis filter and the concept…

Statistical Mechanics · Physics 2014-02-10 Manon Michel , Sebastian C. Kapfer , Werner Krauth

In this paper, we suggest a novel sampling method for Monte Carlo molecular simulations. In order to perform efficient sampling of molecular systems, it is advantageous to avoid extremely high energy configurations while also retaining the…

Computational Physics · Physics 2019-07-18 Katsuhiro Endo , Daisuke Yuhara , Kenji Yasuoka

In this paper, we provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme. The formulation does not involve any terminal ingredients, thus allowing for a simple implementation…

Optimization and Control · Mathematics 2024-12-04 Joscha Bongard , Julian Berberich , Johannes Köhler , Frank Allgöwer

We present a detailed study of a filtering method based upon Dirac quasi-zero-modes in the adjoint representation. The procedure induces no distortions on configurations which are solutions of the euclidean classical equations of motion. On…

High Energy Physics - Lattice · Physics 2015-05-27 Margarita Garcia Perez , Antonio Gonzalez-Arroyo , Alfonso Sastre

The so-called phaseless quantum Monte-Carlo method currently offers one of the best performing theoretical framework to investigate interacting Fermi systems. It allows to extract an approximate ground-state wavefunction by averaging…

Strongly Correlated Electrons · Physics 2017-03-31 Olivier Juillet , Alexandre Leprévost , Jérémy Bonnard , Raymond Frésard

It has been revealed by mean-field theories and computer simulations that the nature of the collapse transition of a polymer is influenced by its bending stiffness $\epsilon_{\rm b}$. In two dimensions, a recent analytical work demonstrated…

Soft Condensed Matter · Physics 2009-11-13 Jie Zhou , Zhong-Can Ou-Yang , Haijun Zhou

We consider a dynamical elasto-plasticity system with Kelvin--Voigt viscosity and linear kinematic hardening of Melan--Prager type. The model is formulated in a variational framework in which a constraint set for the stress evolves in time…

Analysis of PDEs · Mathematics 2026-03-02 Yoshiho Akagawa , Kazunori Matsui

Standard MCMC methods can scale poorly to big data settings due to the need to evaluate the likelihood at each iteration. There have been a number of approximate MCMC algorithms that use sub-sampling ideas to reduce this computational…

Computation · Statistics 2020-09-29 Joris Bierkens , Paul Fearnhead , Gareth Roberts
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